Biometrics By The Border...11. An Approach To Poisson Mixed Models For -Omics Expression Data 12....

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Transcript of Biometrics By The Border...11. An Approach To Poisson Mixed Models For -Omics Expression Data 12....

Page 1: Biometrics By The Border...11. An Approach To Poisson Mixed Models For -Omics Expression Data 12. Bayesian Spatial Estimation When Areas Are Few 13. Knowledge-Guided Generalized Biclustering
Page 2: Biometrics By The Border...11. An Approach To Poisson Mixed Models For -Omics Expression Data 12. Bayesian Spatial Estimation When Areas Are Few 13. Knowledge-Guided Generalized Biclustering

BiometricsByTheBorder

InternationalBiometricsSocietyAustralasianRegionConferenceKingscliff,NSW

26-30thNovember2017

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BiometricsByTheBorder1. Welcome2. ProgrammeAtAGlance

1. Monday2. Tuesday3. Wednesday4. Thursday

3. ProgrammeAndAbstractsForMonday27thOfNovember1. AgriculturalAndAgri-EnvironmentalStatisticsWithSupportOf

GeospatialInformation,MethodologicalIssues2. DevelopingARegulatoryDefinitionForTheAuthenticationOf

ManukaHoney3. OnTestingRandomEffectsInLinearMixedModels4. AnalysingDigestionData5. APermutationTestForComparingPredictiveValuesInClinical

Trials6. ChallengesAndOpportunitiesWorkingAsAConsultingStatistician

WithAFoodScienceResearchGroup7. RobustSemiparametricInferenceInRandomEffectsModels8. RobustPenalizedLogisticRegressionThroughMaximumTrimmed

LikelihoodEstimator9. AMulti-StepClassifierAddressingCohortHeterogeneityImproves

PerformanceOfPrognosticBiomarkersInComplexDisease10. HowToAnalyseFiveDataPointsWithFun11. AnApproachToPoissonMixedModelsFor-OmicsExpressionData12. BayesianSpatialEstimationWhenAreasAreFew13. Knowledge-GuidedGeneralizedBiclusteringAnalysisForIntegrative

–OmicsAnalysis14. UnderstandingTheVariationInHarvesterYieldMapDataFor

EstimatingCropTraits15. CitizenScienceToSurveillance:EstimatingReportingProbabilities

OfExoticInsectPests16. IntroductionTo“Deltagen”-AComprehensiveDecisionSupportTool

ForPlantBreedersUsingRAndShiny17. EstimatingNitrousOxideEmissionFactors

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4. ProgrammeAndAbstractsForTuesday28thOfNovember1. ClusterCapture-Recapture:ANewFrameworkForEstimating

PopulationSize2. PropensityScoreApproachesInThePresenceOfMissingData:

ComparisonOfBalanceAndTreatmentEffectEstimates3. VisualisingModelSelectionStabilityInHigh-DimensionalRegression

Models4. TheMissingLink:AnEquivalenceResultForLikelihoodBased

MethodsInMissingDataProblems5. DimensionalityReductionOfLIBSDataForBayesianAnalysis6. AnalysisOfMelanomaDataWithAMixtureOfSurvivalModels

UtilisingMulti-ClassDLDAToInformMixtureClass7. ForecastingHotspotsOfPotentiallyPreventableHospitalisationsWith

SpatiallyAggregatedLongitudinalHealthData:AllSubsetModelSelectionWithANovelImplementationOfRepeatedK-FoldCross-Validation

8. IdentifyingClustersOfPatientsWithDiabetesUsingAMarkovBirth-DeathProcess

9. ChallengesAnalysingCombinedAgriculturalFieldTrialsWithPartiallyOverlappingTreatments

10. AHiddenMarkovModelForSleepStageDetectionUsingRawTri-AxialWristActigraphy

11. SparsePhenotypingDesignsForEarlyStageSelectionExperimentsInPlantBreedingPrograms

12. ComparisonsOfTwoLargeLong-TermStudiesInAlzheimer'sDisease

13. AOne-StageMixedModelAnalysisOfCanolaChemistryTrials14. ASemi-ParametricLinearMixedModelsForLongitudinally

MeasuredFastingBloodSugarLevelOfAdultDiabeticPatients15. IndividualAndJointAnalysesOfSugarcaneExperimentsToSelect

TestLines5. ProgrammeAndAbstractsForWednesday29thOfNovember

1. StatisticsOnStreetCorners2. EstimatingOverdispersionInSparseMultinomialData3. AssessingMudCrabMeatFullnessUsingNon-InvasiveTechnologies4. AnalysisOfMultivariateBinaryLongitudinalData:Metabolic

SyndromeDuringMenopausalTransition5. StatisticalAnalysisOfCoastalAndOceanographicInfluencesOnThe

QueenslandScallopFishery

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6. SavedByTheExperimentalDesign:TestingBycatchReductionAndTurtleExclusionDevicesInThePngPrawnTrawlFishery

7. RethinkingBiosecurityInspections:ACaseStudyOfTheAsianGypsyMoth(AGM)InAustralia

8. SubtractiveStabilityMeasuresForImprovedVariableSelection9. AComparisonOfMultipleImputationMethodsForMissingDataIn

LongitudinalStudies10. SpeciesDistributionModellingForCombinedDataSources11. AFactorAnalyticMixedModelApproachForTheAnalysisOf

GenotypeByTreatmentByEnvironmentData12. TheImpactOfCohortSubstanceUseUponLikelihoodOf

TransitioningThroughStagesOfAlcoholAndCannabisUseAndUseDisorder:FindingsFromTheAustralianNationalSurveyOnMentalHealthAndWell-Being

13. TheLASSOOnLatentIndicesForOrdinalPredictorsInRegression14. Whole-GenomeQTLAnalysisForNestedAssociationMapping

Populations15. AnAsymmetricMeasureOfPopulationDifferentiationBasedOnThe

SaddlepointApproximationMethod16. FastAndApproximateExhaustiveVariableSelectionForGLMsWith

APES17. OrderSelectionOfFactorAnalyticModelsForGenotypeX

EnvironmentInteraction18. MultipleSampleHypothesisTestingOfTheHumanMicrobiome

ThroughEvolutionaryTrees19. StatisticalStrategiesForTheAnalysisOfLargeAndComplexData20. OptimalExperimentalDesignForFunctionalResponseExperiments21. ToPCAOrNotToPCA22. AnEvaluationOfErrorVarianceBiasInSpatialDesigns23. NewModel-BasedOrdinationDataExplorationToolsFor

MicrobiomeStudies24. AlwaysRandomize?25. ComparingClassicalCriteriaForSelectingIntra-ClassCorrelated

FeaturesForThree-ModeThree-WayData26. ExploringTheSocialRelationshipsOfDairyGoats27. BayesianSemi-ParametricSpectralDensityEstimationWith

ApplicationsToTheSouthernOscillationIndex28. EfficientMultivariateSensitivityAnalysisOfAgriculturalSimulators29. BayesianHypothesisTestsWithDiffusePriors:CanWeHaveOur

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CakeAndEatItToo?6. ProgrammeAndAbstractsForThursday30thOfNovember

1. AGeneralFrameworkForFunctionalRegressionModelling2. HockeySticksAndBrokenSticks–ADesignForASingle-Arm,

Placebo-Controlled,Double-Blind,RandomizedClinicalTrialSuitableForChronicDiseases

3. BoundingIVEstimatesUsingMediationAnalysisThinking4. CorrelatedBivariateNormalCompetingRisks---Structuring

EstimationInAnIll-PosedProblem5. BayesianRegressionWithFunctionalInequalityConstraints6. GeneticAnalysisOfRenalFunctionInAnIsolatedAustralian

IndigenousCommunity7. ThePerformanceOfModelAveragedTailAreaConfidenceIntervals8. BiasCorrectionInEstimatingProportionsByPooledTesting9. TheParametricCureFractionModelOfOvarianCancer10. TheSkillings-MackStatisticForRanksDataInBlocks

7. PosterAbstracts1. Multi-EnvironmentTrialAnalysisOfAgronomyTrialsUsing

EstablishedPlantPopulationDensity2. AdventuresInDigitalAgricultureInNewZealand3. ModellingCanopyGreennessOverTimeUsingSplinesAndNon-

LinearRegression4. OnTestingMarginalHomogeneityForSquareContingencyTables

WithOrdinalCategories5. AFactorAnalyticApproachToModellingDiseaseProgression

AcrossLeafLayersAndTime6. AssociatingStrawStrengthWithLikelihoodOfHeadLossInBarley

ForWesternAustralia7. ADeepLearningNeuralNetworkModelToIdentifyTheImportant

GenesInMetastaticBreastCancerFromCensoredMicroarrayData8. UsingRAndShinyToDevelopWebBasedSamplingApplications

ForTheAgriculturalAndEducationSectors9. GraphicalNetworkAnalysesInformsBiomarkerDiscoveryVia

QuantificationOfKeyDiseaseRelatedConnections10. Spatio-TemporalCorticalBrainAtrophyPatternsOfAlzheimer's

Disease11. EmpiricalModellingOfFruitFirmnessChangeDuringColour

Conditioning

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WelcomeWelcometothe2017BiometricsbytheBorderconferenceinKingscliff,NewSouthWales.AswemeethereonthelandoftheBundjalungpeople,weacknowledgethetraditionalcustodiansofthelandandpayourrespecttotheElderspastandpresent.

IbidaspecialwelcometoallwhotravelledfarfromcountriessuchasBelgium,Brazil,China,Denmark,Germany,Italy,Japan,Nigeria,SouthAfrica,USAandVietnamtocomplementthemajorityfromAustraliaandNewZealand.

TheconferencewillbeopenedbythecurrentPresidentofIBS,ElizabethThompsonandclosedbytheincomingPresident,LouiseRyan.In-between,wehavesixoutstandingkeynotespeakerswhosettheframeworkforanexcitinganddiversescientificprogram.Iinviteyoualltoattendpresentationsontopicsfamiliartoyourresearchandtoexplorewhatisdoneinotherareas.

Wehaveaveryhealthynumberofstudentsandearlycareerresearchersamongthemorethan120delegatesattending,indicatinghowpopular,importantandattractivedatarichresearchcontinuestobeintheyearstocome.

Thisconferencewillbeasuccessbecauseofexcellentworkbymany,inparticularbyRossDarnell,MelissaDobbie,ClaytonForknall,AlisonKellyandBethanyMacdonald,whoformthelocalorganizingcommittee;byJamesCurran(ChairoftheScientificProgram),GarthTarrandEstherMeenkenwhoassistedJames;byWarrenMuller(Treasurer),DavidBaird(Secretary)andbyHansHockeywhoisourofficialphotographer,soalwayssmilewhenheisaround.

LastbutnotleastIthankourPlatinumsponsorsACEMS,GRDCandtheUniversityofSydneyfortheirgenerousfinancialsupport,manyaspectsoftheconferenceareonlypossiblebecauseoftheirhelp.

SamuelMueller,PresidentIBS-AR

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ProgrammeAtAGlance

Monday

Time Monday27/11850 Housekeeping900 Welcome

SamuelMueller,PresidentTraditonalwelcometoAustraliabyarepresentativeofthe

BundjalungElizabethThompson,PresidentoftheIBS

940 AgriculturalAndAgri-EnvironmentalStatisticsWithSupportOfGeospatialInformation,MethodologicalIssues

ElisabettaCarfagnaUniversityofBologna,Italy

1030 MorningTea(30minutes)

Modelling Sampling,ComplexSurveys,AndMixedModels

1100 DevelopingARegulatoryDefinitionForTheAuthenticationOfManukaHoneyClaireMcDonald,MinistryforPrimaryIndustries,NZ

OnTestingRandomEffectsInLinearMixedModelsAlanWelsh,ANU

1120 AnalysingDigestionDataMaryannStaincliffe,AgResearchLtd.

APermutationTestForComparingPredictiveValuesInClinicalTrialsKoujiYamamoto,OsakaCityUniversity

1140 ChallengesAndOpportunitiesWorkingAsAConsultingStatisticianWithAFoodScienceResearchGroupM.GabrielaBorgognone,QueenslandDepartmentof

RobustSemiparametricInferenceInRandomEffectsModelsMichaelStewart,UniversityofSydney

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AgricultureandFisheries1200 RobustPenalizedLogistic

RegressionThroughMaximumTrimmedLikelihoodEstimatorTongWang,ShanxiMedicalUniversty,China

1220 Lunch(1hour10minutes)1330 AMulti-StepClassifierAddressingCohortHeterogeneity

ImprovesPerformanceOfPrognosticBiomarkersInComplexDisease

JeanYangUniversityofSydney

General/Education Genetics1420 HowToAnalyseFiveDataPoints

WithFunPaulineO'Shaughnessy,UniversityofWollongong

AnApproachToPoissonMixedModelsFor-omicsExpressionDataIreneZeng,UniversityofAuckland

1440 BayesianSpatialEstimationWhenAreasAreFewAswiAswi,QueenslandUniversityofTechnology

Knowledge-guidedGeneralizedBiclusteringAnalysisforintegrative–omicsAnalysisQiLong,UniversityofPennsylvania

1500 UnderstandingTheVariationInHarvesterYieldMapDataForEstimatingCropTraitsDeanDiepeveen,CurtinUniversity

CitizenScienceToSurveillance:EstimatingReportingProbabilitiesOfExoticInsectPestsPeterCaley,CSIRO

1520 Afternoontea(30minutes)Computing/BigData EnvironmentalStatistics

1550 IntroductionTo“Deltagen”-AComprehensiveDecisionSupportToolForPlantBreedersUsingRAndshinyDongwenLuo,AgResearchLtd.

EstimatingNitrousOxideEmissionFactorsAlasdairNoble,AgResearchLtd.

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1630 LightningTalksfollowedbyPosterSessionKindlySponsoredbytheGrainsResearchandDevelopment

Corporation

Tuesday

Time Tuesday28/11850 Housekeeping900 ClusterCapture-Recapture:ANewFrameworkForEstimating

PopulationSizeRachelFewster

UniversityofAuckland950 MorningTea(30minutes)

MissingData HighDimensionalData1030 PropensityScore

ApproachesInThePresenceOfMissingData:ComparisonOfBalanceAndTreatmentEffectEstimatesJannahBaker,TheGeorgeInsituteforGlobalHealth

VisualisingModelSelectionStabilityinHigh-DimensionalRegressionModelsGarthTarr,UniversityofSydney

1050 TheMissingLink:AnEquivalenceResultForLikelihoodBasedMethodsInMissingDataProblemsFirouzehNoghrehchi,UniversityofNewSouthWales

DimensionalityReductionofLIBSDataforBayesianAnalysisAnjaliGupta,UniversityofAuckland

Medical Modelling1110 AnalysisofMelanoma

DatawithaMixtureofSurvivalModelsUtilisingMulti-class

ForecastingHotspotsOfPotentiallyPreventableHospitalisationsWithSpatiallyAggregatedLongitudinalHealthData:AllSubsetModelSelectionWithANovel

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DLDAtoInformMixtureClassSarahRomanes,UniversityofSydney

ImplementationOfRepeatedk-FoldCross-ValidationMatthewTuson,UniversityofWesternAustralia

1130 IdentifyingClustersOfPatientsWithDiabetesUsingaMarkovBirth-DeathProcessMugdhaManda,UniversityofAuckland

ChallengesAnalysingCombinedAgriculturalFieldTrialsWithPartiallyOverlappingTreatmentsKerryBell,QueenslandDepartmentofAgricultureandFisheries

1150 AHiddenMarkovModelForSleepStageDetectionUsingRawTri-AxialWristActigraphyMichelleTrevenen,UniversityofWesternAustralia

SparsePhenotypingDesignsForEarlyStageSelectionExperimentsInPlantBreedingProgramsNicoleCocks,UniversityofWollongong

1210 ComparisonsOfTwoLargeLong-TermStudiesInAlzheimer'sDiseaseCharleyBudgeon,UniversityofWesternAustralia

AOne-StageMixedModelAnalysisOfCanolaChemistryTrialsDanielTolhurst,UniversityofWollongong

1230 ASemi-ParametricLinearMixedModelsForLongitudinallyMeasuredFastingBloodSugarLevelOfAdultDiabeticPatientsLegesseKassaDebusho,UniversityofSouthAfrica

IndividualAndJointAnalysesOfSugarcaneExperimentsToSelectTestLinesRenataAlcardeSermarini,UniversityofSaoPaulo,Brazil

1250 Lunch(1hour10minutes)

Wednesday

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Time Wednesday29/11850 Housekeeping900 StatisticsOnStreetCorners

DianneCookMonashUniversity

950 MorningTea(30minutes)Modelling Fisheries BiometricsI

1030 EstimatingOverdispersioninSparseMultinomialDataFarzanaAfroz,UniverityofOtago

AssessingMudCrabMeatFullnessUsingNon-InvasiveTechnologiesCaroleWright,QueenslandDepartmentofAgricultureandFisheries

AnalysisOfMultivariateBinaryLongitudinalData:MetabolicSyndromeDuringMenopausalTransitionGeoffJones,MasseyUniversity

1050 StatisticalAnalysisOfCoastalAndOceanographicInfluencesOnTheQueenslandScallopFisheryWen-HsiYang,UniversityofQueensland

SavedByTheExperimentalDesign:TestingBycatchReductionAndTurtleExclusionDevicesInThePngPrawnTrawlFisheryEmmaLawrence,CSIRO

RethinkingBiosecurityInspections:ACaseStudyOfTheAsianGypsyMoth(AGM)InAustraliaPetraKuhnert,CSIRO

ModelSelection Agriculture/Horticulture BiometricsII1110 SubtractiveStability

MeasuresForImprovedVariableSelectionConnorSmith,UniversityofSydney

GxEforGenomicPredictionModelsColleenHunt,QueenslandDepartmentofFisheriesandAgriculture

AComparisonOfMultipleImputationMethodsForMissingDataInLongitudinalStudiesMdHamidul

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Huque,MurdochChildrensResearchInstitute

1130 SpeciesDistributionModellingForCombinedDataSourcesIanRenner,UniversityofNewcastle

AFactorAnalyticMixedModelApproachForTheAnalysisOfGenotypeByTreatmentByEnvironmentDataLaurenBorg,UniversityofWollongong

TheImpactOfCohortSubstanceUseUponLikelihoodOfTransitioningThroughStagesOfAlcoholAndCannabisUseAndUseDisorder:FindingsFromTheAustralianNationalSurveyOnMentalHealthAndWell-BeingChriannaBharat,NationalDrugandAlcoholResearchCentre,Australia

1150 TheLASSOOnLatentIndicesForOrdinalPredictorsinRegressionFrancisHui,ANU

Whole-GenomeQTLAnalysisForNestedAssociationMappingPopulationsMariaValeriaPaccapelo,QueenslandDepartmentofAgricultureandFisheries

AnAsymmetricMeasureofPopulationDifferentiationBasedontheSaddlepointApproximationMethodLouise

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McMillan,UniversityofAuckland

1210 FastAndApproximateExhaustiveVariableSelectionForGLMsWithAPESKevinWang,UniversityofSydney

OrderSelectionOfFactorAnalyticModelsForGenotypeXEnvironmentInteractionEmiTanaka,UniversityofSydney

MultipleSampleHypothesisTestingOfTheHumanMicrobiomeThroughEvolutionaryTreesMartinaMincheva,TempleUniversity,USA

1230 Lunch(1hour10minutes)

AGM1340 StatisticalStrategiesfortheAnalysisofLargeAndComplexData

LouiseRyanUniversityofTechnologySydneyand

HarvardT.H.ChanSchoolofPublicHealthDesign Multivariate

1430 OptimalExperimentalDesignforFunctionalResponseExperimentsChristopherDrovandi,QueenslandUniversityofTechnology

ToPCAOrNotToPCACatherineMcKenzie,AgResearchLtd.

1450 AnEvaluationofErrorVarianceBiasinSpatialDesignsEmlynWilliams,ANU

NewModel-BasedOrdinationDataExplorationToolsForMicrobiomeStudiesOlivierThas,GhentUniversity,Belgium

1510 AlwaysRandomize?ChrisBrien,UniversityofSouthAustraliaand

ComparingClassicalCriteriaForSelectingIntra-ClassCorrelated

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UniversityofAdelaide FeaturesForThree-ModeThree-WayDataLynetteHunt,UniversityofWaikato

1530 Afternoontea(20minutes)Agriculture/Horticulture BayesianStatistics

1550 ExploringthesocialrelationshipsofdairygoatsVanessaCave,AgResearchLtd.

BayesianSemi-ParametricSpectralDensityEstimationWithApplicationsToTheSouthernOscillationIndexClaudiaKirch,UniversityofMagdeburg,Germany

1610 EfficientMultivariateSensitivityAnalysisOfAgriculturalSimulatorsDanielGladish,CSIRO

BayesianHypothesisTestsWithDiffusePriors:CanWeHaveOurCakeAndEatItToo?JohnOrmerod,UniversityofSydney

Thursday

Time Thursday30/11850 Housekeeping900 AGeneralFrameworkForFunctionalRegressionModelling

SonjaGrevenLMUniversity,Munich

950 MorningTea(30minutes)

Biostatistics TheoryAndMethods

1030 HockeySticksAndBrokenSticks–ADesignForASingle-Arm,Placebo-Controlled,Double-Blind,RandomizedClinicalTrialSuitableForChronicDiseases

BoundingIVEstimatesUsingMediationAnalysisThinking

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HansHockey,BiometricsMatters TheisLange,UniversityofCopenhagen

1050 CorrelatedBivariateNormalCompetingRisks---StructuringEstimationInAnIll-PosedProblemMalcolmHudson,MacquarieUniversity

BayesianRegressionwithFunctionalInequalityConstraintsJoshuaBon,UniversityofWesternAustralia

Genetics/Medical TheoryAndMethods

1110 GeneticAnalysisOfRenalFunctionInAnIsolatedAustralianIndigenousCommunityRussellThomson,WesternSydneyUniversity

ThePerformanceOfModelAveragedTailAreaConfidenceIntervalsPaulKabaila,LaTrobeUniversity

1130 DeconstructingTheInnateImmuneComponentOfAMolecularNetworkOfTheAgingFrontalCortexEllisPatrick,UniversityofSydney

BiasCorrectionInEstimatingProportionsbyPooledTestingGrahamHepworth,UniversityofMelbourne

1150 TheParametricCureFractionModelofOvarianCancerSerifatFolorunso,UniversityofIbadan,Nigeria

TheSkillings-MackStatisticForRanksDataInBlocksJohnBest,UniversityofNewcastle

1210 ConferenceCloseLouiseRyan

1230 Lunch(1hour10minutes)

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ProgrammeAndAbstractsForMonday27thOfNovember

Keynote:Monday27th9:40Mantra

AgriculturalAndAgri-EnvironmentalStatisticsWithSupportOfGeospatialInformation,MethodologicalIssues

ElisabettaCarfagnaUniversityofBologna

Agri-environmentaltrade-offsareissuescriticalforpolicymakerschargedwithmanagingbothfoodsupplyandthesustainableuseoftheland.Reliabledataarecrucialfordevelopingeffectivepoliciesandforevaluatingtheirimpact.However,oftenthereliabilityofagriculturalandagro-environmentalstatisticsislow.

Duetothetechnologicaldevelopment,inthelastdecades,differentkindsofgeospatialdatahavebecomeeasilyaccessibleatdecreasingpricesandhavestartedtobeanimportantsupporttostatisticsproductionprocess.

Inthispaper,wefocusonmethodologicalissuesrelatedtotheuseofgeospatialinformationforsamplingframeconstruction,sampledesign,stratification,grounddatacollectionandestimationofagriculturalandagri-environmentalparameters.Particularattentionisdevotedtotheimpactofspatialresolutionofdata,changeofsupportaggregationanddisaggregationofspatialdata,whenremotesensingdata,GlobalPositioningSystemsandGeographicInformationSystems(GIS)areusedforproducingagriculturalandagro-environmentalstatistics.

Monday27th11:00Narrabeen

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DevelopingARegulatoryDefinitionForTheAuthenticationOfManukaHoney

ClaireMcDonald1,SuzanneKeeling1,MarkBrewer2,andSteveHathaway11MinistryforPrimaryIndustries

2BioSS

ManukahoneyisapremiumexportproductfromNewZealandthathasbeenunderscrutinyduetoclaimsoffraud,adulterationandmislabelling.Althoughthereareseveralindustryapproachesfordefiningmanukahoney,thereiscurrentlynoscientificallyrobustdefinitionsuitableforuseinaregulatorysetting.Assuch,ensuringtheauthenticityofmanukahoneyischallenging.

HerewepresenttheresultsofathreeyearscienceprogrammewhichdevelopedscientificallyrobustdefinitionsformonofloralandmultifloralmanukahoneyproducedinNewZealand.Theprogrammeinvolved:selectingappropriatemarkerstoidentifyhoneysourcedfromLeptospermumscoparium(manuka),establishingplantandhoneyreferencecollections,developingtestmethodstodeterminethelevelsofthemarkersandanalysingthedatageneratedtodevelopthedefinitions.

Thesuitabilityof16markers(chemicalandDNA-based)wereevaluatedforuseinaregulatorydefinitionformanukahoney.Plantsampleswerecollectedfromtwofloweringseasonsrepresentingbothmanukaandnon-manukaspeciesfrombothNewZealandandAustralia.Honeysamples,alsorepresentingmanukaandnon-manukafloraltypes,weresourcedfromsevenNewZealandproductionseasons.Additionally,honeysamplesweresourcedfromanother12countriestoenablecomparison.Allsamplesweretestedforthemarkersbeingevaluatedusingthedevelopedtestmethods.

ThemethodofCART(ClassificationandRegressionTrees)wasusedtodevelopthemonofloralandmultifloralmanukahoneydefinitions.TheCARToutputswerefurtherprocessedusingasimulationapproachtodeterminethesensitivityandtherobustnessofthedefinitions.Thedefinitionsuseacombinationof5markers(4chemicaland1DNA)atsetthresholdstoclassifyasampleasmanukahoneyorotherwise.Wediscussthepracticalitiesofusingthescience-baseddefinitionswithinaregulatorycontext.

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Monday27th11:00Gunnamatta

OnTestingRandomEffectsInLinearMixedModels

AlanWelsh1,FrancisHui1,andSamuelMueller21ANU

2UniversityofSydney

Wecanapproachproblemsinvolvingrandomeffectsinlinearmixedmodelsdirectlythroughtherandomeffectsorthroughparameterssuchasthatthevariancecomponentsthatdescribethedistributionsoftherandomeffects.Bothapproachesareusefulbutleadtodifferentissues.Forexample,workingwiththerandomeffectsraisesquestionsofhowtoestimatethemandcanmeandealingwithalargenumberofrandomeffects,whileworkingwithvariancecomponentsleadstotestinghypothesesontheboundaryoftheparameterspace.Inbothcases,findinggoodapproximationstothenulldistributionoftheteststatisticcanbechallengingsomodernapproachesoftenrelyonsimulation.Inthistalk,were-examinetheF-testbasedonlinearcombinationsoftheresponses,fortestingrandomeffectsinlinearmixedmodels.Wepresentageneralderivationofthetest,highlightitscomputationspeed,itsgenerality,anditsexactnessasatest,andreportempiricalstudiesintothefinitesampleperformanceofthetest.Weconcludethepresentationbyreportingourlatestresultsfromongoingresearchthatinvestigatesconnectionsbetweentestingandmodelselectionbydiscussingsometestsofsignificanceofrandomeffectsandexploringtheirrelationshiptomodelselectionprocedures.

Monday27th11:20Narrabeen

AnalysingDigestionData

MaryannStaincliffe,DebbieFrost,MustafaFarouk,andGuojieWuAgResearch

Foodtechnologistareinterestedinunderstandingifaddinggrainand/orvegetablestobeefincreasesthedigestibilityovermeatalone.Digestibilityof

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meatwasmeasuredatupto4hoursusingapepsinandpancreatininvitromodel.Thismethodinvolvesusinggelsinlanes,wherethedensityofthecolourofthegelisanindicatorofthepresenceofaproteinorpeptideatthatlevelofkilodalton(kDa).TypicallykDaabove12kDaareconsideredtobetheproteinsandlowerthanthatarethepeptides.Inthepastwehaveanalysedthesedatausingtwoapproaches.Thefirstapproachistoselect4or5bandsthatexpectedtobeimportantandthenuseamixedeffectsmodeltocomparethemeanTraceQuantityofeachofthebands,whereMeattype,Additivetype(vegetableand/orgrain)andTimepointsarefixedfactorsandthegelisarandomeffect.ThesecondapproachistofitacurvetothechangeintheproportionofTraceQuantityabove12kDafromTimezero.Theproblemwiththesetwoapproachesisthatwestruggletoprovideaconsistentinterpretationoftheresults.Therefore,wewillexplorealternativemethodsforanalysingthistypeofdata.

Monday27th11:20Gunnamatta

APermutationTestForComparingPredictiveValuesInClinicalTrials

KoujiYamamotoandKanaeTakahashiOsakaCityUniversity

Screeningtestsordiagnostictestsareimportantforearlydetectionandtreatmentofdisease.Therearefourwell-knownmeasurements,sensitivity(SE),specificity(SP),positivepredictivevalue(PPV)andnegativepredictivevalue(NPV)indiagnosticstudies.ForcomparingSEs/SPs,McNemartestiswidelyused,butthereareonlyfewmethodsforthecomparisonofPPVs/NPVs.Moreover,allofthesemethodsarebasedonlarge-sampletheory.

So,inthistalk,firstly,weinvestigatetheperformanceofthosemethodswhenthesamplesizeissmall.Inaddition,weproposeapermutationtestforcomparingtwoPPVs/NPVswecanapplyevenifthesamplesizeissmall.Finally,weshowtheperformanceoftheproposedmethodwithsomeexistingmethodsviasimulationstudies.

Monday27th11:40Narrabeen

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ChallengesAndOpportunitiesWorkingAsAConsultingStatisticianWithAFoodScienceResearchGroup

M.GabrielaBorgognoneQueenslandDepartmentofAgricultureandFisheries

Whenanestablishedresearchgrouphasbeenfunctioningformanyyearswithoutastatisticianasanintegralpartoftheteam,welcomingoneintothegroupcanpresentchallengesaswellasopportunitiesforallinvolved.

Challengesfortheresearchgroupinclude,forexample,involvingthestatisticianatthebeginningofthestudyinsteadofoncetheexperimentshavebeencompletedandthedatacollected;acquiringorincreasingknowledgeofexperimentaldesignprinciples;understandingthelimitationsofsomestatisticalanalyses,expandingtherangeofmethodstheyfeelfamiliarwith,andlearningwhen/howtoapplyeachone;andimprovingthepresentationofresultsinthiserawherepoorpresentationisperpetuatedbythegenerallackofsoundstatisticalmethodsintheliteratureoftheresearcharea.Challengesforthestatisticianinclude,forexample,overcominghis/herlackofgeneralknowledgeoftheunderlyingscientificareaanditsspecificvocabulary;determiningwhatexperimentaldesignswouldworkfromapracticalpointofview;developingunderstandingoftheirscientificquestions,datamanagementpractices,andtypesofdatacollected;navigatingthevarioussoftwaretheyuseandcheckingtheiradequaciesandlimitations;and,aboveall,communicatingwithpatienceandperseverance.

Correspondingly,allchallengespresentopportunitiesforimprovementandcollaborationbetweenscientistsandstatisticians.Workingasateamsupportsadecisionmakingprocessthatisrelevanttoindustryandthatisbasedongoodstatisticalpractices.Additionally,ithelpsscientistsbecomemorestatisticallyawareandempowered.AbitmorethanayearagoIstartedworkingasaconsultingstatisticianwithafoodscienceresearchgroup.InthispresentationIwillsharesomeofthechallengesandtheopportunitiestoincorporategoodstatisticalpracticeIhaveidentified,aswellassomeoftheimprovementswehavemadesofarworkingtogetherinthispartnership.

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Monday27th11:40Gunnamatta

RobustSemiparametricInferenceInRandomEffectsModels

MichaelStewart1andAlanWelsh21UniversityofSydney

2ANU

Wereportonrecentworkusingsemiparametrictheorytoderiveprocedureswithdesirablerobustnessandefficiencypropertiesinthecontextofinferenceconcerningscaleparametersforrandomeffectmodels.

Monday27th12:00Gunnamatta

RobustPenalizedLogisticRegressionThroughMaximumTrimmedLikelihoodEstimator

HongweiSun,YuehuaCui,andTongWangShanxiMedicalUniversity

Penalizedlogisticregressionisusedtoidentifygeneticmarkersformanyhigh-dimensionaldatasetssuchasingeneexpression,GWAS,DNAmethylationstudiesandsoon.Butoutlierssometimesoccurduetomisseddiagnosisormisdiagnosisofsubjects,heterogeneityofsamples,technicalproblemsinexperimentsorotherproblems.Theycangreatlyinfluencetheestimationofpenalizedlogisticregression.Fewstudiesfocusontherobustnessofpenalizedmethodswhentheresponsevariableiscategorical,whichisstandardinmedicalresearch.ThisstudyproposedarobustLASSO-typepenalizedlogisticregressionbasedonmaximumtrimmedlikelihood(MTL-LASSO).Thedefinitionofbreakdownpoint(BDP)forpenalizedlogisticregressionwasgivenanditspropertyfortheproposedmethodwasproved.AmodificationofFAST-LTSalgorithmswasusedtoimplementtheestimation.Thereweightedstepwas

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addedtoimproveperformancewhileguaranteeingrobustness.Thesimulationstudyshowstheproposedmethodcanresistagainstoutliers.Arealdatasetaboutgeneexpressionprofilesofmultiplesclerosispatientsandhealthycontrolswasanalysed.OutliersinthecontrolgroupidentifiedbyreweightedMTL-LASSObehavedifferentlyfromothers.Itunveilstheremaybeheterogeneityproblemincontrolgroup.Amuchbetterfitisobtainedafterremovingoutliers.

Keynote:Monday27th13:30Mantra

AMulti-StepClassifierAddressingCohortHeterogeneityImprovesPerformanceOfPrognosticBiomarkersInComplexDisease

JeanYangUniversityofSydney

Recentstudiesincancerandothercomplexdiseasescontinuetohighlighttheextensivegeneticdiversitybetweenandwithincohorts.Thisintrinsicheterogeneityposesoneofthecentralchallengestopredictingpatientclinicaloutcomeandthepersonalizationoftreatments.Here,wewilldiscusstheconceptofclassifiabilityobservedinmulti-omicsstudieswhereindividualpatients’samplesmaybeconsideredaseitherhardoreasytoclassifybydifferentplatforms,reflectedinmoderateerrorrateswithlargeranges.Wedemonstrateinacohortof45AJCCstageIIImelanomapatientsthatclinico-pathologicbiomarkerscanidentifythosepatientsthataremostlikelytobemisclassifiedbyamolecularbiomarker.Theprocessofmodellingtheclassifiabilityofpatientswasthenreplicatedinindependentdatafromotherdiseases.

Amulti-stepprocedureincorporatingthisinformationnotonlyimprovedclassificationaccuracyoverallbutalsoindicatedthespecificclinicalattributesthathadmadeclassificationproblematicineachcohort.Instatisticalterms,ourstrategymodelscohortheterogeneityviatheidentificationofinteractioneffectsinahighdimensionalsetting.Atthetranslationallevel,thesefindingsshowthatevenwhencohortsareofmoderatesize,includingfeaturesthatexplainthepatient-specificperformanceofaprognosticbiomarkerinaclassificationframeworkcansignificantlyimprovethemodellingandestimationofsurvival,

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aswellasincreaseunderstanding.

Monday27th14:20Narrabeen

HowToAnalyseFiveDataPointsWithFun

PaulineO'Shaughnessy1,StephenRobson2,andLouiseRawlings21UniversityofWollongong

2ANU

While“bigdata”isoneofthebiggestbuzzwords,weoccasionallycomecrossdatawithveryfewdatapoints.ThisdatasetisfromastudyoftheincidenceratesofcommonlyperformedmedicalproceduresInAustralia,whichisavailableonlyatthestatelevel.WehavefivestatesinAustraliathusfivedatapoints.Sowhatcanwedowhenweonlyhavefivedatapoints?Oneofthenoveltyapproachesistofitthedatawitharegressionmodel.Howevergiventhechallengingnatureofthedatawithsmallsize,noguaranteecanbeplacedonthesatisfactoryofthelinearityandhomoscedasticityassumptionofthelinearregression,inturns,theinferencefromthestandardlinearmodeltheoryisnolongervalid.Doublebootstrapisusedtoprovidesolutiontothevalidstatisticalinferenceforthebestlinearapproximationfortherelationshipbetweenvariablesinanassumption-leanregressionsetting.

Monday27th14:20Gunnamatta

AnApproachToPoissonMixedModelsFor-OmicsExpressionData

IreneSuilanZengandThomasLumleyUniversityofAuckland

Weareinterestedinregressionmodelsformultivariatedatafromhigh-throughputbiologicalassays('omic'data).Thesedatahavecorrelationsbetweenvariables,andmayalsocomefromstructuredexperiments,soageneralisedlinearmixedmodelisappropriatetofittheexperimentalvariablesanddifferenttypesofomicsdata.However,thenumberofvariablesisoftenlargerthanthe

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numberofobservations:astructuredcovariancemodelisnecessaryandsparsityinductionisbiologicallyappropriate.InthispresentationwedescribeanapproachtoPoissonmixedmodels,suitableforRNAseqgeneexpressiondata,basedontranscript-specificrandomeffectswithasparseprecisionmatrix.Weshowbysimulationsthattheoptimalsparsenesspenaltyforregressionmodellingisnotthesameasintheusualgraphestimationproblemandcomparesomeestimationstrategiesinsimulations.

Monday27th14:40Narrabeen

BayesianSpatialEstimationWhenAreasAreFew

AswiAswi,SusannaCramb,EarlDuncan,andKerrieMengersenQueenslandUniversityofTechnlogy

Spatialmodellingwhentherearefew(<20)smallareascanbechallenging.Bayesianmethodscanbebeneficialinthissituationduetotheeaseofspecifyingstructureandadditionalinformationthroughpriors.However,careisneededasthereareoftenfewerneighboursandmoreedges,whichmayinfluenceresults.HereweinvestigateBayesianspatialmodelspecificationwhentherearefewareas,firstthroughasimulationstudy(numberofareasrangingfrom4to2500)andthenapplytoacasestudyondenguefeverin2015inMakassar,Indonesia(14areas).FourdifferentBayesianspatialmodelsnamely,anindependentmodeland3modelsbasedonaCAR(ConditionalAutoregressive)prior:theBesag,York&Mollié,Leroux,andalocalisedmodel(augmentstheCARpriorwithaclustermodelusingpiecewiseconstantintercepts)wereapplied.Dataweregeneratedforthesimulationstudyconsideringlowandhighspatialautocorrelationandlowandhighdiseaseincidence.ModelgoodnessoffitwascomparedusingDevianceInformationCriteria.AnalysisofvarianceandBonferroni'smethodwerealsousedtodeterminewhichmodelsweresignificantlydifferent.Thesimulationstudyshowedmodelsdifferedintheirperformancemainlyintwosituations:1.Whentherewereatleast25areasandboththediseaserateandspatialautocorrelationwaslow,and2.Forallareasizeswhentherewaslowspatialautocorrelationbutahighoveralldiseaserate.Likewise,resultsfromthecasestudyshowedthatallfourmodelsperformedsimilarly.Thisisprobablyduetothelownumberofareasandalowdisease

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incidence.

Monday27th14:40Gunnamatta

Knowledge-GuidedGeneralizedBiclusteringAnalysisForIntegrative–OmicsAnalysis

ChanggeeChang1,YizeZhao2,MingyaoLi1,andQiLong11UniversityofPennsylvania

2CornellUniversity

Advancesintechnologyhaveenabledgenerationofmultipletypesof-omicsdatainmanybiomedicalandclinicalstudies,anditisdesirabletopoolsuchdatainordertoimprovethepowerofidentifyingimportantmolecularsignaturesandpatterns.However,suchintegrativeanalysespresentnewanalyticalandcomputationalchallenges.Toaddresssomeofthesechallenges,weproposeaBayesiansparsegeneralizedbiclusteringanalysis(GBC)whichenablesintegratingmultipleomicsmodalitieswithincorporationofbiologicalknowledgethroughtheuseofadaptivestructuredshrinkagepriors.Theproposedmethodscanaccommodatebothcontinuousanddiscretedata.MCMCandEMalgorithmsaredevelopedforestimation.Numericalstudiesareconductedtodemonstratethatourmethodsachieveimprovedfeatureselectionandpredictioninidentifyingdiseasesubtypesandlatentdrivers,comparedtoexistingmethods.

Monday27th15:00Narrabeen

UnderstandingTheVariationInHarvesterYieldMapDataForEstimatingCropTraits

DeanDiepeveen1,KarynReeves1,AdrianBaddeley1,andFionaEvans21CurtinUniversity

2MurdochUniversity

Ourrecentexploratoryresearchinvolvesextractingtangiblecroptraitsfromimagessuchasyield-maps.ResearchbyDiepeveenetal(2012)demonstrated

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thatgeneticinformationcanbeextractedfromnear-infrared(NIR)imagesusingboththeimplicitknowledgeofdata,environmentaldataandusingamultivariateapproach.Yieldmapdataisgeo-referenceddataofgrain-yieldthatisgeneratedbyaharvestercuttingthecrop,threshingthestrawandextractingthegrain.Ourresultsshowthattheyield-mapdatahassignificantissuesassociatedwithit.Oneissueisthedelayfromtimeofcuttingthecroptoenteringintothestorage-binformeasurement.Thisisdependentonspeedoftheharvesterandiscompoundedwithmaintainingacriticalvolumegoingthroughtheharvestertooperateefficiency.Therearealsoissueswithvariationfromthedensityandplantsizeofthecropwithinthepaddockbeingharvested.Ourpreliminaryresultsjusthighlightthesignificantchallengesinextractingprecisecroptraitsfromyieldmapdata.

Monday27th15:00Gunnamatta

CitizenScienceToSurveillance:EstimatingReportingProbabilitiesOfExoticInsectPests

PeterCaley1,MarijkeWelvaert2,andSimonBarry11CSIRO

2UniversityofCanberra

Upuntilmid-2016,citizenscienceuploadstotheAtlasofLivingAustraliaincludedc.400bugspecies,andc.1,000beetlespecies.Giventheshorttimeperiod(c.3years)overwhichmostoftheserecordshaveaccumulated,thisrepresentsaconsiderablereportingeffort.Thekeyappliedquestionfromabiosecuritycontextishowthislevelofreportingtranslatestothedetectionandreportingofexoticinsectpestsintheeventofanincursion.

Weuseacase-controldesigntomodeltheprobabilityofexistinginsectspeciesbeingreportedviacitizensciencechannelsfeedingintotheAtlasofLivingAustralia.Theeffectofinsectfeatures(size,colour,pattern,morphology)andgeographicdistributiononreportingratesareexploredasexplanatoryvariables.Wethenapplythemodeltoexotichighprioritypestspeciestopredicttheirreportingratesintheeventoftheirintroduction.

Monday27th15:50Narrabeen

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IntroductionTo“Deltagen”-AComprehensiveDecisionSupportToolForPlantBreedersUsingRAndShiny

DongwenLuoandZulfiJahuferAgResearch

Theobjectiveofthispresentationistointroduceauniquenewplantbreedingdecisionsupportsoftwaretool"DeltaGen",implementedinRanditspackageShiny.DeltaGenprovidesplantbreederswithasingleintegratedsolutionforexperimentaldesigngeneration,dataqualitycontrol,statisticalandquantitativegeneticanalyses,breedingstrategyevaluation/simulationandcostanalysis,patternanalysis,indexselectionandunderlyingbasictheoryonquantitativegenetics.Thissoftwaretoolcouldalsobeusedasateachingresourceinplantbreedingcourses.DeltaGenisavailableasFreewareonthelink:http://agrubuntu.cloudapp.net/shiny-apps/PlantBreedingTool/

Monday27th15:50Gunnamatta

EstimatingNitrousOxideEmissionFactors

AlasdairNobleandTonyVanDerWeerdenAgResearch

NitrousOxide(N2O)isanimportantgreenhousegaswithaglobalwarmingpotentialnearly300timesthatofcarbondioxide.UndertheKyotoProtocolNewZealandisrequiredtoreportagreenhousegasinventoryannuallywhichincludesN2O.InNewZealand,95%ofN2Oemissionsarederivedfromnitrogen(N)inputstoagriculturalsoils(e.g.animalexcretaandfertiliser).FieldexperimentsareconductedtoestimatetheseN2Oemissions,wheredataiscollatedandanalysedfollowingastandardmethodologytodetermineemissionfactors,whichestimatetheamountofN2OlostperunitofNappliedtosoil.Howeverforindividualdatasetstherearesomeaspectsofthedataareincompatiblewiththeproposedmodelsosomeadhocadjustmentsaremade.AmorerigorousBayesianapproachisproposedandsomeresultswillbediscussed.

http://agrubuntu.cloudapp.net/shiny-apps/PlantBreedingTool/
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ProgrammeAndAbstractsForTuesday28thOfNovember

Keynote:Tuesday28th9:00Mantra

ClusterCapture-Recapture:ANewFrameworkForEstimatingPopulationSize

RachelFewsterUniversityofAuckland

Askanywildlifemanager:theirfirstburningquestionis"Howmanyarethere?",andtheirsecondis"Aretheytrendingupwardsordownwards?"Capture-recaptureisoneofthemostpopularmethodsforestimatingpopulationsizeandtrends.Asthenamesuggests,itreliesonbeingabletoidentifythesameanimaluponmultiplecaptureoccasions.Thepatternofcapturesandrecapturesamongidentifiedanimalsisusedtoestimatethenumberofanimalsnevercaptured.

Physicallycapturingandtagginganimalscanbeadangerousandstressfulexperienceforboththeanimalsandtheirhumaninvestigators-orifittranspiresthattheanimalsactuallyenjoyit,biasedinferencemayresult.Consequently,researchersincreasinglyfavournon-invasivesamplingusingnaturaltagsthatallowanimalstobeidentifiedbyfeaturessuchascoatmarkings,droppedDNAsamples,acousticprofiles,orspatiallocations.Theseinnovationsgreatlybroadenthescopeofcapture-recaptureestimationandthenumberofcapturesamplesachievable.However,theyareimperfectmeasuresofidentity,effectivelysacrificingsamplequalityforquantityandaccessibility.Asaresult,capture-recapturesamplesnolongergeneratecapturehistoriesinwhichthematchingofrepeatedsamplestoasingleidentityiscertain.Instead,theygeneratedatathatareinformative---butnotdefinitive---aboutanimalidentity.

Iwilldescribeanewframeworkfordrawinginferencefromcapture-recapturestudieswhenthereisuncertaintyinanimalidentity.Intheclustercapture-

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recaptureframework,weassumethatrepeatedsamplesfromthesameanimalwillbesimilar,butnotnecessarilyidentical,toeachother.Overlapisalsopossiblebetweenclustersofsamplesgeneratedbydifferentanimals.Wetreatthesampledataasaclusteredpointprocess,andderivethenecessaryprobabilisticpropertiesoftheprocesstoestimateabundanceandotherparametersusingaPalmlikelihoodapproach.

Becauseitavoidsanyattemptsatexplicitsample-matching,theclustercapture-recapturemethodcanbeveryfast,takingmuchthesametimetoanalysemillionsofsample-comparisonsasitdoestoanalysehundreds.Iwilldescribeapreliminaryframeworkforabundanceestimationfromacousticmonitoring.Clustercapture-recapturecanalsobeusedforbehaviouralstudies,andIwillshowanexampleusingcamera-trapdatafromapartially-markedpopulationofforestshiprats.

Tuesday28th10:30Narrabeen

PropensityScoreApproachesInThePresenceOfMissingData:ComparisonOfBalanceAndTreatmentEffectEstimates

JannahBaker1,TimWatkins1,2,andLaurentBillot1,31TheGeorgeInsituteforGlobalHealth

2UniversityofSydney3UniversityofNewSouthWales

Theuseofpropensityscoremethodscanpotentiallyimprovebalancebetweengroupsinobservationalstudydata,thusminimisingconfounding.However,afrequentproblemwithsuchstudiesisthepresenceofmissingdata.Wecomparethreeapproachestogeneratingthepropensityscoreaccountingformissingdatawithinthecontextofaclinicalcasestudyexaminingtheeffectoftelemonitoringonhypertension.Overall,4,642patientsdiagnosedwithhypertensionreceivingonlinehealthsupportinMyHealthGuardian–atelephonechronicdiseasesupportprogramofferedbyprivatehealthinsurerHCF–wereofferedatelemonitoringintervention.Ofthese,2,729acceptedandstartedtreatmentbetweenJuly2014toApril2015(designated“cases”),and1,913declined

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(designated“controls”).Datawereavailablefromcasesandcontrolsonseveralbaselinevariablesincludingdemographic,lifestyleandclinicalcharacteristics.Outcomeswerethenumberofhospitalisations,totallengthofstayandtotalcostofhospitalisationbetween1Januaryto31December2016.Propensityscoremethodswereusedtobalancebaselinevariablesbetweengroups.Threeapproacheswereusedtogeneratepropensityscoresfromalogisticregressionmodelaccountingformissingdata:1)categorisationofallvariableswith“Missing”asacategory,2)multipleimputationoftreatmenteffect(MIte)wheretreatmenteffectestimatesarecombinedover20imputeddatasets,and3)multipleimputationofpropensityscore(MIps)wherepropensityscoresarefirstaveragedoverimputeddatasetspriortoestimationoftreatmenteffects.Thepropensityscorefromeachapproachwasthenusedintwoways:a)matchingcasestocontrols,andb)inverseprobabilityoftreatmentweighting(IPTW).Thebalanceachievedbyeachapproachwascomparedusingstandardiseddifferencesofmeansandproportionsinbaselinecharacteristicsbetweengroups.Thetreatmenteffectestimatesfromeachapproachwerealsocompared.Thediscussionwillcanvasourfindingsandrecommendationsforhandlingmissingdatawhenusingpropensityscoreapproaches.

Tuesday28th10:30Gunnamatta

VisualisingModelSelectionStabilityInHigh-DimensionalRegressionModels

GarthTarrUniversityofSydney

ThemplotRpackageprovidesaprovidesanimplementationofmodelstabilityandvariableinclusionplotsforresearcherstousetobetterinformthevariableselectionprocess.Theinitialfocuswasonexhaustivesearchesthroughthemodelspace,however,thisquicklybecomesinfeasibleforhighdimensionalmodels.Analternativeapproachforhighdimensionalmodelsistocombinebootstrapmodelselectionwithregularisationprocedures.Thereexistanumberoffastandefficientmethodregularisationmethodsforvariableselectioninhighdimensionalregressionsettings.Wehaveimplementedvariableinclusionplotsandmodelstabilityplotsusingtheglmnetpackage.Wedemonstratetheutilityofthemplotpackageinidentifyingstableregularisedmodelselectionchoices

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withrespecttotwomainsourcesofuncertainty.Firstly,byresamplingthedataweareabletodeterminehowoftenvariousmodelsarechosenwhenthedatachanges.Secondly,weareabletoevaluatehowoftencompetingmodelsarechosenacrossarangeofvaluesforthetuningparameter.Exploringthesetwosourcesofuncertaintyinmodelselectiongeneratesalargeamountofrawdatathatneedstobeprocessed.Themplotpackageprovidesavarietyofmethodstovisualisethisrawdatatohelpinformaresearcher'smodelselectionchoice.

Tuesday28th10:50Narrabeen

TheMissingLink:AnEquivalenceResultForLikelihoodBasedMethodsInMissingDataProblemsFirouzehNoghrehchi,JakubStoklosa,SpiridonPenev,andDavidI.Warton

UniversityofNewSouthWales

Multipleimputationandmaximumlikelihoodestimation(viatheexpectation-maximizationalgorithm)aretwowell-knownmethodsreadilyusedforanalysingdatawithmissingvalues.Thesetwomethodsareoftenconsideredasbeingdistinctfromoneanother,duetotheirconstructionforestimationandtheirtheoreticalproperties.Weshowthatthereisacloserelationshipbetweenthetwomethods.Specifically,weshowthatatypeofmultipleimputationcanbeunderstoodasastochasticexpectation-maximisationapproximationtomaximumlikelihood.Asaresult,wecanexploretheapplicationofarangeoflikelihood-basedtoolsinthemultipleimputationcontextinordertoimproveitsperformance.Inparticular,wedevelopinformationcriteriaforselectinganimputationmodelgivenasetofcompetingmodels,andaflexiblelikelihoodratiotestwhenmodelsarefittedbymultipleimputation.Wedemonstrateourmethodsonrealandsimulateddatasets.

Tuesday28th10:50Gunnamatta

DimensionalityReductionOfLIBSDataForBayesianAnalysis

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AnjaliGupta1,JamesCurran1,SallyCoulson2,andChristopherTriggs11UniversityofAuckland

2ESR

In2004,AitkenandLucypublishedanarticledetailingatwo-levellikelihoodratioformultivariatetraceevidence.Thismodelhasbeenadoptedinanumberofforensicdisciplinessuchastheinterpretationofglass,drugs(MDMA),andink.Moderninstrumentationiscapableofmeasuringmanyelementsinverylowquantitiesand,notsurprisingly,forensicscientistswishtoexploitthepotentialofthisextrainformationtoincreasetheweightofthisevidence.Theissue,fromastatisticalpointofview,isthattheincreaseinthenumberofvariables(dimension)intheproblemleadstoincreaseddatademandtounderstandboththevariabilitywithinasource,andinbetweensources.Suchinformationwillcomeintime,butusuallywedon’thaveenough.Onesolutiontothisproblemistoattempttoreducethedimensionalitythroughmethodssuchasprincipalcomponentanalysis.Thispracticeisquitecommoninhighdimensionalmachinelearningproblems.Inthistalk,IwilldescribeastudywhereweattempttoquantifytheeffectsofthisthisapproachontheresultinglikelihoodratiosusingdataobtainedfromaLaserInducedBreakdownSpectroscopy(LIBS)instrument.

Tuesday28th11:10Narrabeen

AnalysisOfMelanomaDataWithAMixtureOfSurvivalModelsUtilisingMulti-ClassDLDAToInformMixtureClass

SarahRomanes,JohnOrmerod,andJeanYangUniversityofSydney

MelanomaisaprevalentskincancerinAustralia,withcloseto14000newcasesestimatedtobediagnosedin2017.Survivaltimesaremarkedlydifferentfromoneindividualtothenext.Inparticular,thereappearstobethreeclassesofsurvivaloutcome.Thistalkconsidersintegratingsurvivaltimedatawithmicroarraygeneexpressiondata.Weconstructahybridmodelthatseamlesslyintegratesathree-classlineardiscriminantanalysismodel,mixtureofparametric

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survivalmodels,andmodelselectioncomponents.Wefitthismodelusingavariationalexpectationmaximization(VEM)approach.Ourmodelselectioncomponentnaturallysimplifiesasafunctionoflikelihoodratiostatisticsallowingnaturalcomparisonswithtraditionalhypothesistestingmethods.Wecompareourmethodwithseveralnaïveapproacheswhichonlyaddressestheclassificationaspectorsurvivalmodelaspectinisolation.

Tuesday28th11:10Gunnamatta

ForecastingHotspotsOfPotentiallyPreventableHospitalisationsWithSpatiallyAggregatedLongitudinalHealthData:AllSubsetModelSelectionWithANovelImplementationOfRepeatedK-FoldCross-Validation

MatthewTuson,BerwinTurlach,KevinMurray,MeiRuuKok,AlistairVickery,andDavidWhyatt

UniversityofWesternAustralia

Itissometimesdifficulttotargetindividualsforhealthinterventionduetolimitedinformationontheirbehaviourandriskfactors.Insuchcasesplace-basedinterventionstargetinggeographical‘hotspots’withhigherthanaverageratesofhealthserviceutilisationmaybeeffective.Manystudiesexistexaminingpredictorsofhotspots,butoftendonotconsiderthatplace-basedinterventionsaretypicallycostlyandtaketimetodevelopandimplement,andhotspotsoftenregresstothemeanintheshort-term.Long-termgeographicalforecastingofhotspotsusingvalidatedstatisticalmodelsisessentialineffectivelyprioritisingplace-basedhealthinterventions.

Existingmethodsforecastinghotspotstendtoprioritisepositivepredictedvalue(i.e.correctpredictions)attheexpenseofsensitivity.Thisworkintroducesmethodstodevelopmodelsoptimisingbothpositivepredictedvalueandsensitivityconcurrently.Thesemethodsutilisespatiallyaggregated

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administrativehealthdata,WAcensuspopulationdata,andABSgeographicboundaries,combiningallsubsetmodelselectionwithanovelimplementationofrepeatedcross-validationforlongitudinaldata.Resultsfrommodelsforecasting3-yearhotspotsforfourpotentiallypreventablehospitalisationsarepresented,namely:typeIIdiabetesmellitus,heartfailure,highriskfoot,andchronicobstructivepulmonarydisease(COPD).

Tuesday28th11:30Narrabeen

IdentifyingClustersOfPatientsWithDiabetesUsingAMarkovBirth-DeathProcess

MugdhaManda,ThomasLumley,andSusanWellsUniversityofAuckland

Estimatingdiseasetrajectorieshasincreasinglybecomemoreessentialtoclinicalpractitionerstoadministereffectivetreatmenttotheirpatients.Apartofdescribingdiseasetrajectoriesinvolvestakingpatients'medicalhistoriesandsociodemographicfactorsintoaccountandgroupingthemintosimilargroups,orclusters.Advancesincomputerisedpatientdatabaseshavepavedawayforidentifyingsuchtrajectoriesinpatientsbyrecordingapatient'smedicalhistoryoveralongperiodoftime(longitudinaldata):westudieddatafromthePREDICT-CVDdataset,anationalprimary-carecohortfromwhichpeoplewithdiabetesfrom2002-2015wereidentifiedthroughroutineclinicalpractice.WefittedaBayesianhierarchicallinearmodelwithlatentclusterstotherepeatedmeasurementsofHbA1candeGFR,usingtheMarkovbirth-deathprocessproposedbyStephens(2000)tohandlethechangesindimensionalityasclusterswereaddedorremoved.

Tuesday28th11:30Gunnamatta

ChallengesAnalysingCombinedAgriculturalFieldTrialsWithPartiallyOverlapping

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Treatments

KerryBellandMichaelMumfordQueenslandDepartmentofAgricultureandFisheries

Tomakerecommendationsonwhichmanagementpracticeshavethepotentialtoincreasecropyieldthereneedstobeaconsistentpatterndemonstratedacrosstrialsfrommanyenvironments.Thispresentationconsidersacasestudylookingat31mungbeantrialsinnorthernAustralianfrom2014to2016.Thetrialsdidnotalwayshaveconsistentfactors(e.g.variety,rowspacingortargetplantdensity)orevenconsistentfactorlevels.Toovercometheissueofinconsistentfactors,environmentsweredefinedasthecombinationofsite,yearandanymanagementfactorsnotcommonacrosstrials(e.g.timeofsowing,irrigation,fertiliser).

Therewerenumerousfullfactorialcombinationswithinsubsetsofthedatathatcouldbeconsideredforinvestigationsothefirstchallengewastodeterminewhichfactorialcombinationstofocusontobestaddresstheresearchquestionsandreportingrequirements.Oncethiswasdetermined,allthedatafromthetrialsthatcontributedtothefactorialwereincludedinacombinedanalysisusinglinearmixedmodels.Inthismodel,thefactorialofinterestwaspartitionedinthetestoffixedeffectswhileeachtrials’designparametersandresidualvarianceswereestimatedusingallthedatafromeachtrial.Anexampleoftheabovementionedfactorialcombinationsisenvironmentbyrowspacingforoneparticularvariety.Thenextchallengewasthatwithsomanyenvironmentstherewasusuallyanenvironmentbyrowspacinginteractionwhichwasnotusefulformakingrecommendationsaboutrowspacing.

Clusteringofenvironmentsallowedforminggroupsthatdidnothaveasignificantinteractionbetweenrowspacingandenvironment.Thesegroupswerethengeneralisedtotypesofenvironmentswithcertainresponsestorowspacing.

Tuesday28th11:50Narrabeen

AHiddenMarkovModelForSleepStageDetectionUsingRawTri-AxialWrist

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Actigraphy

MichelleTrevenen1,KevinMurray1,BerwinTurlach1,LeonStraker2,andPeterEastwood1

1UniversityofWesternAustralia2CurtinUniversity

Sleepisacomplexyetorganisedprocessconsistingofregularcyclesofsleepstages.Thesestagesarerapid-eyemovementandnon-rapideyemovement(lightsleepandslow-wavesleep).Sleepstagingisofgreatimportanceinthephysiologicalworldassleepdisordersoccurinaround20%ofthepopulationandareassociatedwithamultitudeofserioushealthimplicationsandconsiderableeconomicburden.HiddenMarkovmodelshavebeensuccessfullyusedintheclassificationofindividualsleepstagesmeasuredbypolysomnography,whichisconsideredthe'goldstandard'inassessingsleep,however,itisintrusiveandcostly.

Actigraphyisincreasinglybeingconsideredasanon-intrusiveandcosteffectivealternativemethodtoobjectivelymeasuresleeppatterns.However,thereislimitedresearchontheabilityofactigraphytodetectindividualsleepstages,furthermore,thisresearchindicatesthatthesecurrentmethodologiesdonothavetheabilitytodoso.Currentactigraphicapproachestosleepdetectionusefiltereduni-axialdatameasuredwithalowsamplingrate,whereas,rawtri-axialdatameasuredathighsamplingratesisfrequentlyusedintheassessmentofday-timeactivities.

Usingsimultaneouslymeasuredactigraphyandpolysomnographydatafrom100healthyyoungadultsintheWesternAustralianPregnancyCohortStudywecreatedandvalidatedanalgorithmtodeterminesleepstagesutilisingraw,tri-axialaccelerationdatafromwristactigraphy.Tenfeaturevariableswerecreatedfromeach30-secondblockofdataand50subjectswereusedtotrainthehiddenMarkovmodelwiththefeaturevariablesusedasinputparameters.Theremaining50subjectswereusedtovalidatethetrainedhiddenMarkovmodelagainstpolysomnography.

Validationsuggestedthatourmodelisabletoclassifysleepstagesusingrawtri-axialactigraphydata.Theseresultsdemonstratethatactigraphy-basedhiddenMarkovmodelscanfeasiblybeusedforautomaticsleepstaging.

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Tuesday28th11:50Gunnamatta

SparsePhenotypingDesignsForEarlyStageSelectionExperimentsInPlantBreedingPrograms

NicoleCocks,AlisonSmith,DavidButler,andBrianCullisUniversityofWollongong

Theearlystagesofcerealandpulsebreedingprogramstypicallyinvolveinexcessof500testlines.Thetestlinesarepromotedthroughaseriesoftrialsbasedontheirperformance(yield)andotherdesirabletraitssuchasheat/droughttolerance,diseaseresistance,etc.Itisthereforeimportanttoensurethedesign(andanalysis)ofthesetrialsareefficientinordertoappropriatelyandaccuratelyguidethebreedersthroughtheirselectiondecisions,untilonlyasmallnumberofelitelinesremain.

ThedesignofearlystagevarietytrialsinAustraliaprovidedthemotivationfordevelopinganewdesignstrategy.Thepreliminarystagesoftheseprogramshavelimitedseedsupply,whichlimitsthenumberoftrialsandreplicatesoftestlinesthatcanbesown.Traditionally,completelybalancedblockdesignsorgridplotdesignsweresownatasmallnumberofenvironmentsinordertoselectthehighestperforminglinesforpromotiontothelaterstagesoftheprogram.Givenourunderstandingofvariety(i.e.line)byenvironmentinteraction,thisapproachisnotasensibleoroptimaluseofthelimitedresourcesavailable.

Anewmethodtoallowforalargernumberofenvironmentstobesampledforsituationswhereseedsupplyislimitedandnumberoftestlinesislargewillbediscussed.Thisstrategywillbereferredtoassparsephenotyping,whichisdevelopedwithinthelinearmixedmodelframeworkasamodel-baseddesignapproachtogeneratingoptimaltrialdesignsforearlystageselectionexperiments.

Tuesday28th12:10Narrabeen

ComparisonsOfTwoLargeLong-Term

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StudiesInAlzheimer'sDisease

CharleyBudgeonUniversityofWesternAustralia

TheincidenceofAlzheimer’sdisease(AD),theleadingcauseofdementia,ispredictedtoincreaseatleastthreefoldby2050.Curingthisdiseaseisaglobalpriority.Currently,twomajorstudiesareattemptingtogainfurtherunderstandingofthisdisease;theAlzheimer'sDiseaseNeuroimagingInitiative(ADNI)andtheAustralianImaging,BiomarkerandLifestyleStudy(AIBL).Wedescribethesetwocohortstoassesstheimpactofcombiningthemtoprovidealargercohortforanalyses.

Aninitialcomparisonoftheprotocolswascarriedoutandrecruitmentstrategieswereshowntobemarginallydifferentbetweenthestudies.Inclusioncriteriaspecifiedagesbetween55and90yearsinADNIand>65yearsinAIBL.Marginallydifferentspecificationsfordiseasestageclassificationsofhealthycontrols(HC),mildcognitivelyimpaired(MCI)andADindividualswereobserved,forexample,differentMini-MentalStateExam(MMSE)cut-offs.However,bothstudieshadADdiagnosissupportedbytheNINDS/ARDAcriteria.BaselinecharacteristicswerecomparedbetweenADNIandAIBLcohorts.Overall,AIBLhadmoreHCscomparedtoADNI(69%vs30%),butfewerMCIindividuals(12%vs50%).TheADNIcohorthadahigherlevelofeducationandgenerally,withinadiseaseclassification,therewereminimaldifferencesinbaselineage,sex,MMSE,andPreclinicalAlzheimerCognitiveComposite(PACC)scores.

LongitudinalanalysescomparedthechangeovertimeforthetwocohortsanddiseaseclassificationsforPACCandMMSE.TherewerenosignificantdifferencesincohortswithintheHCandMCIgroups,butwithintheADgroup,subjectsintheADNIcohorthadgenerallyhigherpredictedPACCandMMSEscoresovertimethanthoseinAIBL.

OurresultssuggestthereisthepotentialtocombinetheADNIandAIBLcohortsforanalysispurposestoprovideonemorepowerfuldataset;however,considerationshouldbetakenforsomemeasures.

Tuesday28th12:10Gunnamatta

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AOne-StageMixedModelAnalysisOfCanolaChemistryTrials

DanielTolhurst,KyMathews,AlisonSmith,andBrianCullisUniversityofWollongong

TheNationalVarietyTrials(NVT)programisusedbyplantbreedingcompaniestoevaluatetheyieldpotentialofnewcropvarietiesindependentlyacrossalargerangeofAustraliangrowingconditions.BycomparisonwiththeremainingNVTcrops,growerdecisionsarefurthercomplicatedincanolabecauseofitsvulnerabilitytotheinfestationofweeds.Ameasurehistoricallyusedbyfarmersforthemanagementofweedsistheapplicationofaherbicide(chemistry)treatment.Thechoiceofchemistryisimportantasitrestrictsvarietyselectiontothosebredwiththespecifictolerance.ThesetofvarietiescurrentlyevaluatedinNVTaretoleranttooneofthreechemistries,namelyimidazolinone(I;butmarketedasClearfield),glyphosate(RoundupReady;R)ortriazine(T),orhavenospecifictolerance(i.e.conventionalcanola;C).Consequently,canolahasamorecomplextestingregimethantheremainingNVTcropsaseachtrialhasanestedtreatmentstructureinvolvingbothchemistriesandvarieties.

CanolatrialsareconductedinlocationsacrosstheAustraliangrainbeltandreflectbestfarmerpracticeforeachdistrict.Everysiteispartitionedintoseveralfieldblocksandplotsareallocatedtothetreatmentsaccordingtoorthogonalblockdesigns.Asprayboomisusedtoadministereachchemistrybutispragmaticinthesensethatlargeareasaretreatedsimultaneously.Thisprecludestheapplicationofdifferentspraystoplotsinthesameblock.Randomisationisthereforerestrictedsovarietiesinasingleblockaretoleranttothesamechemistry.However,asthenumberofchemistriesandblocksareexactlyequal,thereisnoinformationtoestimatetheexperimentalerrorvariationandbotharestatisticallyconfounded.Consequently,growersarelimitedtoevaluatingvarietieswiththesametoleranceascomparisonsacrosschemistriesareinvalid.Thisalsohasimportantimplicationsonthestatisticalanalysis,whichisdiscussedinthistalk.

Tuesday28th12:30Narrabeen

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ASemi-ParametricLinearMixedModelsForLongitudinallyMeasuredFastingBloodSugarLevelOfAdultDiabeticPatients

TafereTilahun1,BelayBirlie1,andLegesseKassaDebusho21JimmaUniversity

2UniversityofSouthAfrica

Thispaperfocusedonlongitudinaldataanalysisoffastingbloodsugar(FBS)levelofadultdiabeticpatientsatJimmaUniversitySpecializedHospitaldiabeticclinicusinganapplicationofsemi-parametricmixedmodel.ThestudyrevealedthattherateofchangeinFBSlevelindiabeticpatients,duetotheclinicinterventions,doesnotcontinueasasteadypacebutchangeswithtimeandweightofpatients.Furthermore,itclarifiedassociationsbetweenFBSlevelandsomecharacteristicsofadultdiabeticpatientsthatweightofadiabetespatienthasasignificantnegativeeffectwhereaspatientgender,age,typeofdiabetesandfamilyhistoryofdiabetesdidnothaveasignificanteffectonthechangeofFBSlevel.Undervariousvariancestructuresofsubject-specificrandomeffects,thesemi-parametricmixedmodelshadbetterfitthanlinearmixedmodel.Thiswaslikelyduetothelocalizedsplines,whichcapturedmorevariabilityinFBSlevelthanthelinearmixedmodel.

Tuesday28th12:30Gunnamatta

IndividualAndJointAnalysesOfSugarcaneExperimentsToSelectTestLines

AlessandraDosSantos1,ChrisBrien2,4,ClariceG.B.Demétrio1,RenataAlcardeSermarini1,5,GuilhermeA.P.Silva3,andSandroR.Fuzatto3

1UniversityofSãoPaulo2UniversityofSouthAustralia

3CTC-Piracicaba4UniverstiyofAdelaide5UniversityofAdelaide

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Intheearlystagesofabreedingprogrammanyfieldtrialsareconducted,consideringseveralsoilandweatherconditions.Inthecaseofsugarcane,theseexperimentsareinstalledusingalargenumberoftestlines,butlimitationsinthefieldandoftheamountofgeneticmaterialdonotallowthereplicationofmany.Thisworkevaluated21trialsfromdifferentregions,fromaBraziliansugarcanebreedingprogram.Eachoftheseexperimentsoccupiedarectangulararrayofaround20rowsby25columnsinmostinstance,theplotswere12mlonger,double-furrowswith0.9mbetweenfurrowswithintheplotand1.5mspacingbetweendifferentplotsand1mbetweencolumns.Allthetrialshadatleast79%oftheareaplantedwithunreplicatedtestlines,theatmost21%oftheplotswereoccupiedbyfourcommercialvarieties,check.Aspecialcheck,interspersedalongdiagonals,wasplantedsystematicallyonadiagonalgrid,theotherthreewereequallyreplicatedandeachreplicatewasspreadoutinthreeneighbouringrowplots.Sevenofthe21experimentshadnosignificantdirectgeneticeffects,11presentedsignificantcompetitionattheresiduallevelandonlyonehadsignificantcompetitionatthegeneticlevel.Thecorrelationbetweentheselectedtestlinesforthedifferentexperimentsinasameregionwaslessthan0.54.However,thegeneticcorrelationwassignificantinthejointanalysesandstrongerthanthatfromtheindividualanalyses.Twosimulationstudieswereperformed:thefirstinvestigatedtheanalysisforasingleexperimentandtheresultsshowthatitisdifficulttofitamodelwhenthereisgeneticcompetitionwithorwithoutresidualcompetition.Thesamedifficultywasobservedinthesecondstudy,whichcomparedtheresultsfromindividualandjointanalyses.Itshowedthat,evenforthejointanalyses,onlyaround45to55%ofthetruebesttestlineswereselected.

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ProgrammeAndAbstractsForWednesday29thOfNovember

Keynote:Wednesday29th9:00Mantra

StatisticsOnStreetCorners

DianneCookMonashUniversity

Perceptualresearchisoftenconductedonthestreet,withconveniencesamplingofpedestrianswhohappentobepassingby.Itisthroughexperimentsconductedusingpasser-bysthatwehavelearnedabouttheeffectofchange-blindness(https://www.youtube.com/watch?v=FWSxSQsspiQ)isinplayoutsidethelaboratory.

Indatascience,plotsofdatabecomeimportanttoolsforobservingpatterns,makingdecisions,andcommunicatingfindings.Butplotsofdatacanbevieweddifferentlybydifferentobservers,andoftenprovokeskepticismaboutwhetherwhatyousee"isreallythere?"Withtheavailabilityoftechnologythatharnessesstatisticalrandomisationtechniquesandinputfromcrowdswecanprovideobjectiveevaluationofstructurereadfromplotsofdata.

Thistalkdescribesaninferentialframeworkfordatavisualisation,andtheprotocolsthatcanbeusedtoprovideestimatesofp-values,andpower.Iwilldiscusstheexperimentsthatwehaveconductedthat(1)showthatthecrowd-sourcingdoesprovideresultssimilartostatisticalhypothesistesting,(2)howthiscanbeusedtoimproveplotdesign,(3)p-valuesinsituationswherenoclassicaltestsexist.Examplesfromecologyandagriculturewillbeshown.

JointworkwithHeikeHofmann,AndreasBuja,DeborahSwayne,HadleyWickham,Eun-kyungLee,MahbubulMajumder,NiladriRoyChowdhury,LendieFollett,SusanVanderplas,AdamLoy,YifanZhao,NathanielTomasetti

https://www.youtube.com/watch?v=FWSxSQsspiQ
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Wednesday29th10:30Narrabeen

EstimatingOverdispersionInSparseMultinomialData

FarzanaAfrozUniversityofOtago

Thephenomenonofoverdispersionariseswhenthedataaremorevariablethanweexpectfromthefittedmodel.ThisissueoftenariseswhenfittingaPoissonorabinomialmodel.Whenoverdispersionispresent,ignoringitmayleadtomisleadingconclusions,withstandarderrorsbeingunderestimatedandoverly-complexmodelsbeingselected.Inourresearchweconsideredoverdispersedmultinomialdata,whichcanariseinmanyresearchareas.Twoapproachescanbeusedtoanalyseoverdispersedmultinomialdata;theuseofthequasilikelihoodmethodorexplicitmodellingoftheoverdispersionusing,forexample,aDirichlet-multinomialorfinite-mixturedistribution.Useofquasilikelihoodhastheadvantageofonlyrequiringspecificationofthefirsttwomomentsoftheresponsevariable.Forsparsedata,suchasinacontingencytablewithmanylowexpectedcounts,useofquasilikelihoodtoestimatetheamountofoverdispersionwillbeparticularlyuseful,asitmaybedifficulttoobtainreliableestimatesoftheparametersinaDirichlet-multinomialorfinite-mixturemodel.Iconsiderfourestimatorsoftheamountofoverdispersioninsparsemultinomialdata,discusstheirtheoreticalpropertiesandprovidesimulationresultsshowingtheirperformanceintermsofbias,varianceandmeansquarederror.

Wednesday29th10:30Gunnamatta

AssessingMudCrabMeatFullnessUsingNon-InvasiveTechnologies

CaroleWright,SteveGrauf,BrettWedding,PaulExley,JohnMayze,andSuePoole

QueenslandDepartmentofAgricultureandFisheries

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Thedecisionofwhetheramudcrabshouldberetainedatharvesthastraditionallybeenbasedonshellhardness.Thisismostcommonlyassessedbyusingthumbpressureappliedtothecarapaceofthemudcrab.Thecarapaceofarecentlymoultedmudcrabwillflexconsiderablyandisthereforereturnedtothewater.Thisassessmenthasalsobeenusedtodividemudcrabsintothreemeatfullnessgrades(A,BandC).ThehighermeatfullnessgradeAmudcrabsfetchagreaterpriceatmarketcomparedtothelowerBandCgrades.Thesubjectivenatureofthisassessmentwillalwaysresultindisputesattheboundariesofthegrades.Bydevelopingamoreobjectivescience-basedmethoddowngradesatthemarketwillbereducedwhileconsumersatisfactionandtheoverallindustryprofitabilitywillincrease.

Ascopingstudywasconductedthatevaluatedinnovativenon-invasivetechnologiestoassessmudcrabmeatfullnessbasedonpercentageyieldrecoveryofcookedmeatfromthedominantindividualmudcrabclaws.Thenon-invasivetechnologiesassessedincludednearinfraredspectroscopy(NIRS),candlingusingvisiblelight,andacousticvelocity.NIRSshowedthemostpotentialandwasreassessedinasecondstudywithslightimprovementstospectracapturemethodsandNIRlightsources.

94livemudcrabsfromtheMoretonBayareawereusedinthesecondstudy.Partialleastsquaresregression(PLS-R)wasperformedtobuildacalibrationmodeltopredictthepercentageyieldrecoveryofcookedmeatbasedonthespectraldata.ThePLS-Rhad and .

Aprincipalcomponentslineardiscriminantanalysis(PC-LDA)wasalsoconductedtodiscriminatebetweenthestandardthreegradesofmudcrabmeatfullness.Thiswascomparedtotheindustrystandardshellhardnessmethod.TheNIRSPC-LDAachievedaminimumof76%correctclassificationforeachofthethreegrades,comparedto24%fortheshellhardnessmethod.

Thenon-invasivetechnologiestrialledalongwiththeresultswillbediscussedinthistalk.

Wednesday29th10:30Bundeena

AnalysisOfMultivariateBinaryLongitudinalData:MetabolicSyndromeDuring

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MenopausalTransition

GeoffJonesMasseyUniversity

Metabolicsyndrome(MetS)isamajormultifactorialconditionthatpredisposesadultstotype2diabetesandCVD.Itisdefinedashavingatleastthreeoffivecardiometabolicriskcomponents:1)highfastingtriglyceridelevel,2)lowhigh-densitylipoproteincholesterol,3)elevatedfastingplasmaglucose,4)largewaistcircumference(abdominalobesity),and5)hypertension.IntheUSStudyofWomen’sHealthAcrosstheNation(SWAN),a15-yearmulti-centreprospectivecohortstudyofwomenfromfiveracial/ethnicgroups,theincidenceofMetSincreasedasmidlifewomenunderwentthemenopausaltransition(MT).AmodelissoughttoexaminetheinterdependentprogressionofthefiveMetScomponentsandtheinfluenceofdemographiccovariates.

Wednesday29th10:50Narrabeen

StatisticalAnalysisOfCoastalAndOceanographicInfluencesOnTheQueenslandScallopFishery

Wen-HsiYang1,AnthonyJ.Courtney2,MichaelF.O’neill2,MatthewJ.Campbell2,GeorgeM.Leigh2,andJerzyA.Filar1

1UniverstiyofQueensland2QueenslandDepartmentofAgricultureandFisheries

Thesaucerscallop(Ylistrumballoti)otter-trawlfisheryusedtobethemostvaluablecommercially-fishedspeciesinQueenslandoceanwaters.Overthelastfewyears,therehasbeengrowingconcernamongfishers,fisherymanagersandscientistsoverthedeclineincatchratesandannualharvest.Aquantitativeassessmentconductedin2016showedthatscallopabundancewasatanhistoriclowlevel.Theassessmentuseddatasourcedfromthefisheryandindependentsurveys.Furtherinformationoncoastalandoceanographicinfluencesareavailableandmayrevealnewfactorsthatinfluencepopulationabundanceof

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scallopsandimprovemanagementofthefishery.Inthisstudy,scallopcatchrateabundancedataandcoastalandphysicaloceanographicvariables(e.g.seasurfacetemperatureanomalies,coastalfreshwaterflowandChlorophyll-a)weremodelledtoidentifyspatialandtemporalenvironmentalprocessesimportantforconsiderationinfisherymanagementprocedures.

Wednesday29th10:50Gunnamatta

SavedByTheExperimentalDesign:TestingBycatchReductionAndTurtleExclusionDevicesInThePngPrawnTrawlFishery

EmmaLawrenceandBillVenablesCSIRO

Intrawlingforprawns,theprawncatchisoftenonlyasmallpartoftheresultsofanyonetrawl,withtheremaindercalled"bycatch".Reducingthebycatchcomponent,whilemaintainingtheprawncatch,isanimportantindustrygoal,primarilyforenvironmentalaccreditationpurposes,butalsoforeconomicreasons.

Wedesignedanat-seatrialfortheGulfofPapuaPrawnFishery,involvingfourvesselseachtowing"quadgear"(thatis,4separate,butlinkedtrawlnets)ineachtrawlshot,over18days.Theexperimentwasdesignedtoassesstheeffectivenessof27combinationsofTurtleExcluderDevices(TEDs)andBycatchReductionDevices,(BRDs),withacontrolnet,withoutanyattacheddeviceasoneofthenetsineachquad.AtBiometrics2015wediscussedhowweusedsimulatedannealingtogenerateahighlyefficientdesign,inseveralstages,tomeetthelargenumberofhighlyspecificlogisticalconstraints.

Thefocusofthistalkwillbetheanalysis,whichalsoprovedsomewhatchallenging.Wewillpresenttheresultsofouranalysisanddemonstratewhyputtingthetimeintothinkingaboutandgeneratinganon-standardexperimentaldesignallowedustoaccommodatethevariousglitchesandmisfortunesthatalwaysseemtohappenatsea.

Wednesday29th10:50Bundeena

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RethinkingBiosecurityInspections:ACaseStudyOfTheAsianGypsyMoth(AGM)InAustralia

PetraKuhnert1,DeanPaini1,PaulMwebaze1,andJohnNielsen21CSIRO

2DepartmentofAgricultureandWaterResources

TheAsiangypsymoth(AGM)(Lymantriadisparasiatica)isaseriousbiosecurityrisktoAustralia’sforestryandhorticulturalindustries.WhilesimilarinappearancetotheEuropeangypsymoth(Lymantriadispardispar),theAsiangypsymothiscapableofflyingupto40kilometresandthereforehasthepotentialtoestablishandspreadinotherareaslikeAustralia.Inaddition,femalesareattractedtolightandwilloviposit(layeggs)indiscriminately.Asaresult,femalesareattractedtoshippingportsatnightandwillovipositonships.Theseshipsthereforehavethepotentialtospreadthismotharoundtheworld.

Thelife-cycleofthemothhasbeenwelldocumentedandisheavilydependentontemperature,witheggsundergoingthreephasesofdiapausebeforehatching.CurrentinspectionsofvesselsarrivingintoAustralianportsfromwhatisdeemedan“atrisk”portisalengthyandcostlyprocess.

ToassisttheDepartmentofAgriculturewiththeirprioritisationofships,wedevelopedanAGMToolintheformofanRShinyAppthat(1)showstheshortestmaritimepathfromanatriskporttoanAustralianportforavesselofinterestand(2)predictstheprobabilityofapotentialhatchandit’sreliabilityusingaclassificationtreemodelthatwasdevelopedtoemulatethelifecycleofthemothfromsimulateddata.Inthistalkwewilldiscussthemethodologythat(1)simulatestheAGMbiologyandpotentialhatchesofeggs,alongwithhowweextractedrelevanttemperaturedatathatwastheprimarydriverofthelifecycleforAGM,and(2)emulatesthissimulateddatausingastatisticalmodel,namelyaclassificationtreetopredicttheprobabilityofapotentialhatch.Wewillalsodiscussabootstrapapproachtoexplorethereliabilityofthepotentialhatchpredicted.

Wednesday29th11:10Narrabeen

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SubtractiveStabilityMeasuresForImprovedVariableSelection

ConnorSmith1,SamuelMüller1,andBorisGuennewig1,21UniversityofSydney

2UniversityofNewSouthWales

ThistalkbuildsupontheInvisibleFence(Jiangetal.,2011)apromisingmodelselectionmethod.Utilizingacombinationofcoeffcient,scaleanddevianceestimatesweareabletoimprovethisresamplingbasedmodelselectionmethodforregressionmodels,bothlinearandgenerallinearmodels.Theintroductionofavariableinclusionplotallowsforavisualrepresentationforthestabilityofthemodelselectionmethodaswellasthevariablesbootstrappedrank.Thesuggestedmethodswillbeappliedtobothsimulatedandrealexampleswithcomparisonsaboutbothcomputationaltimeandeffectivenessmadetoselectionsthroughalternativeselectionprocedures.Wewillreportonourlatestresultsfromongoingworkinscalingupsubtractivestabilitymeasureswhenthenumbersoffeaturesislarge.

References:Jiang,J.,Nguyen,T.,&Rao,J.S.(2011).Invisiblefencemethodsandtheidentificationofdifferentiallyexpressedgenesets.StatisticsandItsInterface,4(3),403-415.

Wednesday29th11:10Bundeena

AComparisonOfMultipleImputationMethodsForMissingDataInLongitudinalStudies

MdHamidulHuque1,KatherineLee1,JulieSimpson2,andJohnCarlin11MurdochChildrensResearchInstitute

2UniversityofMelbourne

Multipleimputation(MI)forimputingmissingdataareincreasinglyusedin

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longitudinalstudieswheredataaremissingduetonon-responseandlosttofollow-up.Standardmultivariatenormalimputation(MVNI)andfullyconditionalspecifications(FCS)aretheprincipleimputationframeworkavailableforimputingcross-sectionalmissingdata.Anumberofmethodshasbeensuggestedintheliteraturetoimputelongitudinaldataincluding(i)useofstandardFCSandMVNIwithrepeatedmeasurementsasseparatedistinctvariables(ii)useofimputationmethodsbasedongeneralizedlinearmixedmodels.NoclearevaluationoftherelativeperformanceofavailableMImethodsinthecontextoflongitudinaldata.Wepresentacomprehensivecomparisonofthealltheavailablemethodsforimputationlongitudinaldatainthecontextofestimatingcoefficientforbothlinearregressionmodelandlinearmixedeffectmodel.Wealsocomparedtheperformanceofthemethodstoimputebothbinaryandcontinuousdata.Atotalof10differentmethods(MVNI,JM-pan,JM-jomo,standardFCS,FCS-twofold,FCS-MTW,FCS-2lnorm,FCS-2lglm,FCS-2ljomoandFCS-Blimp)arecomparedintermsofbias,standarderrorandcoverageprobabilityoftheestimatedregressioncoefficients.Thesemethodsarecomparedusingasimulationstudybasedonapreviouslyconductedanalysisexploringtheassociationbetweentheburdenofoverweightandqualityoflife(QoL)usingdatafromtheLongitudinalStudyofAustralianChildren(LSAC).WefoundthatbothstandardFCSandMVNIprovidereliableestimatesandcoverageoftheregressionparameters.Amongothermethodslinearmixedmodelsbasedmethods,JM-jomoandFCS-Blimpapproachesholdgreatpromise.

Wednesday29th11:30Narrabeen

SpeciesDistributionModellingForCombinedDataSources

IanRenner1andOlivierGimenez21UniversityofNewcastle

2Centred’EcologieFonctionnelleetEvolutive

Increasingly,multiplesourcesofspeciesoccurrencedataareavailableforaparticularspecies,collectedthroughdifferentprotocols.Forsingle-sourcemodels,avarietyofmethodshavebeendeveloped:pointprocessmodelsforpresence-onlydata,logisticregressionforpresence-absencedataobtainedthroughsingle-visitsystematicsurveys,andoccupancymodellingfor

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detection/non-detectiondataobtainedthroughrepeat-visitsurveys.Insituationsforwhichmultiplesourcesofdataareavailabletomodelaspecies,thesesourcesmaybecombinedviaajointlikelihoodexpression.Nonetheless,therearequestionsabouthowtointerprettheoutputfromsuchacombinedmodelandhowtodiagnosepotentialviolationsofmodelassumptionssuchastheassumptionofspatialindependenceamongpoints.

Inthispresentation,Iwillexplorequestionsofinterpretationoftheoutputfromthesecombinedapproaches,aswellasproposeextensionstocurrentpracticethroughtheintroductionofaLASSOpenalty,sourceweightstoaccountfordifferingqualityofdata,andmodelswhichaccountforspatialdependenceamongpoints.ThisapproachwillbedemonstratedbymodellingthedistributionoftheEurasianlynxineasternFrance.

Wednesday29th11:30Gunnamatta

AFactorAnalyticMixedModelApproachForTheAnalysisOfGenotypeByTreatmentByEnvironmentData

LaurenBorg,BrianCullis,andAlisonSmithUniversityofWollongong

Theaccurateevaluationofgenotypeperformanceforarangeoftraits,includingdiseaseresistance,isofgreatimportancetotheproductivityandsustainabilityofmajorAustraliancommercialcrops.Typically,thedatageneratedfromcropevaluationprogrammesarisefromaseriesoffieldtrialsknownasmulti-environmenttrials(METs),whichinvestigategenotypeperformanceoverarangeofenvironments.

Inevaluationtrialsfordiseaseresistance,itisnotuncommonforsomegenotypestobechemicallytreatedagainsttheafflictingdisease.AnimportantexampleinAustraliaistheassessmentofgenotypesforresistancetoblacklegdiseaseincanolacropswhereitiscommonpracticetotreatcanolaseedswithafungicide.Genotypesareeithergrownintrialsastreated,untreatedorasboth.

ThereareanumberofmethodsfortheanalysisofMETdata.Thesemethods,

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however,donotspecificallyaddresstheanalysisofdatawithanunderlyingthree-waystructureofgenotypebytreatmentbyenvironment(GxTxE).Here,weproposeanextensionofthefactoranalyticmixedmodelapproachforMETdata,usingthecanolablacklegdataasthemotivatingexample.

Historicallyintheanalysisofblacklegdata,thefactorialgenotypebytreatmentstructureofthedatawasnotaccountedfor.Entries,whicharethecombinationsofgenotypesandfungicidetreatmentspresentintrials,wereregardedas`genotypes’andatwo-wayanalysisof‘genotypes’byenvironmentswasconducted.

Theanalysisofourexampleshowedthattheaccuracyofgenotypepredictions,andthenceinformationforgrowers,wassubstantiallyimprovedwiththeuseofthethree-wayGxTxEapproachcomparedwiththehistoricalapproach.

Wednesday29th11:30Bundeena

TheImpactOfCohortSubstanceUseUponLikelihoodOfTransitioningThroughStagesOfAlcoholAndCannabisUseAndUseDisorder:FindingsFromTheAustralianNationalSurveyOnMentalHealthAndWell-Being

LouisaDegenhardt1,MeyerGlantz2,ChriannaBharat1,AmyPeacock1,LuiseLago1,NancySampson3,andRonaldKessler31NationalDrugandAlcoholResearchCentre

2NationalInstituteonDrugAbuse3HarvardUniversity

Theaimsofthepresentstudyweretousepopulation-levelAustraliandatatoestimateprevalenceandspeedoftransitionsacrossstagesofalcoholandcannabisuse,abuseanddependence,andremissionfromdisorder,andconsiderthepotentialimpactsthatanindividual’sageandsexcohort’slevelofsubstance

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usepredictedtransitionsintoandoutofsubstanceuse.Dataonlifetimehistoryofuse,DSM-IVusedisorders,andremissionfromthesedisorderswerecollectedfromparticipants(n=8,463)inthe2007AustralianNationalSurveyofMentalHealthandWellbeingusingtheCompositeInternationalDiagnosticInterview.

Lifetimeprevalenceofalcoholuse,regularuse,abuse,dependence,andremissionfromabuseanddependencewere94.1%,64.5%,22.1%,4.0%,16.1%and2.1%,respectively.Unconditionallifetimeprevalenceofcannabisuse,abuse,dependence,andremissionfromabuseanddependencewere19.8%,6.1%,1.9%,4.0%and1.5%.Increasesintheestimatedproportionofpeopleintherespondent’ssexandagecohortwhousedalcohol/cannabisasofagivenageweresignificantlyassociatedwithmosttransitionsfromusethroughtoremissionbeginningatthesameage.Clearassociationsweredocumentedbetweencohort-levelprevalenceofsubstanceuseandpersonalriskofsubsequenttransitionsofindividualsinthecohortfromusetogreatersubstanceinvolvement.Thisrelationshipremainedsignificantoverandaboveassociationsinvolvingtheindividual’sageofinitiation.Thesefindingshaveimportantimplicationsforourunderstandingofthecausalpathwaysintoandoutofproblematicsubstanceuse.

Wednesday29th11:50Narrabeen

TheLASSOOnLatentIndicesForOrdinalPredictorsInRegression

FrancisHui1,SamuelMueller2,andAlanWelsh11ANU

2UniversityofSydney

Manyapplicationsofregressionmodelsinvolveordinalcategoricalpredictors.AmotivatingexampleweconsiderisordinalratingsfromindividualsrespondingtoquestionnairesregardingtheirworkplaceintheHouseholdIncomeandLabourDynamicsinAustralia(HILDA)survey,withtheaimbeingtostudyhowworkplaceconditions(mainandpossibleinteractioneffects)affecttheiroverallmentalwellbeing.Acommonapproachtohandlingordinalpredictorsistotreateachpredictorasafactorvariable.Thiscanleadtoaveryhigh-dimensionalproblem,andhasspurredmuchresearchintopenalizedlikelihoodmethodsfor

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handlingcategoricalpredictorswhilerespectingthemarginalityprinciple.Ontheotherhand,giventheordinalratingsareoftenregardedasmanifestationsofsomelatentindicesconcerningdifferentaspectsofjobquality,thenamoresensibleapproachwouldbetofirstperformsomesortofdimensionreductionbeforeenteringthepredictedindicesintoaregressionmodel.Inappliedresearchthisisoftenperformedasatwo-stageprocedure,andindoingsofailstoutilizetheresponseinordertobetterpredictthelatentindicesthemselves.

Inthistalk,weproposetheLASSOonLatentIndices(LoLI)forhandlingordinalcategoricalpredictorsinregression.TheLoLImodelsimultaneouslyconstructsacontinuouslatentindexforeachorgroupsofordinalpredictorsandmodelstheresponseasafunctionofthese(andotherpredictorsifappropriate)includingpotentialinteractions,withacompositeLASSOtypepenaltyaddedtoperformselectiononmainandinteractioneffectsbetweenthelatentindices.Asasingle-stageapproach,theLoLImodelisabletoborrowstrengthfromtheresponsetoimproveconstructionofthecontinuouslatentindices,whichinturnproducesbetterestimationofthecorrespondingregressioncoefficients.Furthermore,becauseoftheconstructionoflatentindices,thedimensionalityoftheproblemissubstantiallyreducedbeforeanyvariableselectionisperformed.Forestimation,weproposefirstestimatingthecutoffsrelatingtheobservedordinalpredictorstothelatentindices.Thenconditionalonthesecutoffs,weapplyapenalizedExpectationMaximizationalgorithmviaimportancesamplingtoestimatetheregressioncoefficients.AsimulationstudydemonstratestheimprovedpoweroftheLoLImodelatdetectingtrulyimportantordinalpredictorscomparedtobothtwo-stageapproachesandusingfactorvariables,andbetterpredictiveandestimationperformancecomparedtothecommonlyusedtwo-stageapproach.

Wednesday29th11:50Gunnamatta

Whole-GenomeQTLAnalysisForNestedAssociationMappingPopulations

MariaValeriaPaccapelo1,AlisonKelly1,JackChristopher2,andArunasVerbyla3,4

1QueenslandDepartmentofAgricultureandFisheries2QueenslandAllianceforAgricultureandFood

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3Data614CSIRO

Geneticdissectionofquantitativetraitsinplantshasbecomeanimportanttoolinbreedingofimprovedvarieties.ThemostcommonlyusedmethodstomapQTLarelinkageanalysisinbi-parentalpopulationsandassociationmappingindiversitypanels.However,bi-parentalpopulationsarerestrictedintermsofallelicdiversityandrecombinationevents.Despitethefactthatassociationmappingovercomestheselimitations,ithaslowpowertodetectrareallelesassociatedwithatraitofinterest.Multi-parentpopulationssuchasmulti-parentadvancedgenerationinter-cross(MAGIC)andnestedassociationmapping(NAM)populationshavebeendevelopedtocombinestrengthsofbothmappingapproaches,capturingmorerecombinationeventsandallelicdiversitythanbi-parentalpopulationsandinagreaterfrequencythanadiversitypanel.NestedassociationmappingusesmultipleRILfamiliesconnectedbyasinglecommonparent.Suchapopulationstructurepresentssomeadditionalchallengescomparedtotraditionalmapping,inparticularthepopulationdesignandthelargenumberofmolecularmarkersthatneedtobeintegratedsimultaneouslyintotheanalysis.WepresentamethodforQTLmappingforNAMpopulationsadaptedfrommulti-parentwholegenomeaverageintervalmapping(MPWGAIM)wheretheNAMdesignisincorporatedthroughtheprobabilityofinheritingfounderallelesforeverymarkeracrossthegenome.Thismethodisbasedonamixedlinearmodelinaone-stageanalysisofrawphenotypestogetherwithmarkers.Itsimultaneouslyscansthewhole-genomethroughaniterativeprocessleadingtoamulti-locusmodel.TheapproachwasappliedtoawheatNAMpopulationinordertoperformQTLmappingforplantheight.ThemethodwasdevelopedinR,withmaindependenciesbeingtheRpackagesMPWGAIMandasreml.ThisapproachestablishesthebasisforfurtherstudiesandextensionssuchasthecombinationofmultipleNAMpopulations.

Wednesday29th11:50Bundeena

AnAsymmetricMeasureOfPopulationDifferentiationBasedOnTheSaddlepointApproximationMethod

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LouiseMcMillanandRachelFewsterUniversityofAuckland

Inthefieldofpopulationgeneticstherearemanymeasuresofgeneticdiversityandpopulationdifferentiation.ThebestknownisWright'sFst,laterexpandedbyCockerhamandWeir,whichisverywidelyusedasameasureofseparationbetweenpopulations.Morerecentlyamultitudeofothermeasureshavebeendeveloped,fromGsttoD,allwithdifferentfeaturesanddisadvantages.Onethingthesemeasuresallhaveincommonisthattheyaresymmetric,whichistosaythattheFstbetweenpopulationAandpopulationBisthesameasthatbetweenpopulationBandpopulationA.FollowingmyworkonGenePlot,avisualizationtoolforgeneticassignment,Iamnowworkingonthedevelopmentofanasymmetricmeasure,wherethefitofAintoBmaynotbethesameasthefitofBintoA.Thismeasurewillenablethedetectionofscenariossuchas"subsetting",therelationshipbetweenalarge,diversepopulationAandasmallerpopulationBthathasexperiencedgeneticdriftsincebeingseparatedfromA.Themeasurehasseveralfeaturesthatdistinguishitfromexistingmeasures,andisconstructedusingthesamesaddlepointapproximationmethodunderlyingGenePlot,andwhichisusedtoapproximatethemulti-locusgeneticdistributionsofpopulations.

Wednesday29th12:10Narrabeen

FastAndApproximateExhaustiveVariableSelectionForGLMsWithAPES

KevinWang,SamuelMueller,GarthTarr,andJeanYangUniversityofSydney

Obtainingmaximumlikelihoodestimatesforgeneralisedlinearmodels(GLMs)iscomputationallyintensiveandremainsasthemajorobstacleforperformingallsubsetsvariableselection.Exhaustiveexplorationofthemodelspace,evenforamoderatelylargenumberofcovariates,remainsaformidablechallengeformoderncomputingcapabilities.Ontheotherhand,efficientalgorithmsforexhaustivesearchesdoexistforlinearmodels,mostnotablytheleapsandboundalgorithmand,morerecently,themixedintegeroptimisationalgorithm.Inthistalk,wepresentAPES(APproximatedExhaustiveSearch)anewmethodthat

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approximatesallsubsetselectionforagivenGLMbyreformulatingtheproblemasalinearmodel.Themethodworksbylearningfromobservationalweightsinacorrect/saturatedgeneralisedlinearregressionmodel.APEScanbeusedinpartnershipwithanyotherstate-of-the-artlinearmodelselectionalgorithm,thusenabling(approximate)exhaustivemodelexplorationindimensionsmuchhigherthanpreviouslyfeasible.WewilldemonstratethatAPESmodelselectioniscompetitiveagainstgenuineexhaustivesearchviasimulationstudiesandapplicationstohealthdata.Extensionstoarobustsettingisalsopossible.

Wednesday29th12:10Gunnamatta

OrderSelectionOfFactorAnalyticModelsForGenotypeXEnvironmentInteraction

EmiTanaka1,FrancisHui2,andDavidWarton31UniversityofSydney

2ANU3UniversityofNewSouthWales

Factoranalytic(FA)modelsarewidelyusedacrossarangeofdisciplinesowingtocomputationaladvantagesfromdimensionreductionandpossibleabilitytointerpretthefactors.Inplantbreeding,FAmodelprovidesanaturalframeworktomodelthegenotypexenvironmentinteraction.AnFAmodelisdictatedbythenumberoffactors(orderofthemodel).Ahigherorderlendstomoreparametersinthemodelandthisnecessitatestheorderselectiontoachieveparsimony.Weintroduceanorderselectionmethodviatheorderedfactorlasso(OFAL).Weillustrateitsperformancebasedonasimulationonarealwheatyieldmulti-environmentaltrial.

Wednesday29th12:10Bundeena

MultipleSampleHypothesisTestingOfTheHumanMicrobiomeThroughEvolutionaryTrees

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MartinaMincheva1,HongzheLi2,andJunChen31TempleUniversity

2UniversityofPennsylvania3MayoClinic

Nextgenerationsequencingtechnologiesmakeitpossibletosurveymicrobialcommunitiesbysequencingnucleicacidmaterialextractedfrommultiplesamples.Themetagenomicreadcountsaresummarizedasempiricaldistributionsonareferencephylogenetictree.ThedistancebetweenthemisevaluatedbytheKantorovich-Rubinstein(kr)metric,equivalenttothecommonlyusedweightedUniFracdistanceonatree.Thispaperproposesamethodtotestthehypothesisthattwosetsofsampleshavethesamemicrobialcomposition.TheasymptoticdistributionsofthekrdistancebetweenthetwoFrechetmeansandtheFrechetvariancesarederivedandareshowntobeindependent.TheteststatisticisdefinedastheratioofthosedistancesanditisshowntofollowanasymptoticF-distribution.Itsgeneralitystemsfromthefactthatthetestisnonparametricandrequiresnoassumptionsontheprobabilitydistributionsofthecountdata.Itisalsoapplicableforvaryingsetsizesandsamplesizes.ItisanextensionofEvansandMatsen(2012)whosuggestatesttoonlycomparetwosinglesamples.Thecomputationalefficiencyoftheproposedtestcomesfromtheexactasymptoticdistributionoftheproposedteststatistic.Extensivedataanalysisshowsthatthetestissignificantlyfasterthanthepermutation-basedmultivariateanalysisofvarianceusingdistancematrices(permanova)(McArdleandAnderson,2001).Atthesametime,ithascorrecttype1errorsandcomparablepower,whichmakesitpreferableintheanalysisoflargescalemicrobiomedata.

Keynote:Wednesday29th13:40Mantra

StatisticalStrategiesForTheAnalysisOfLargeAndComplexData

LouiseRyan1,2,StephenWright1,3,andHonHwang11UniversityofTechnologySydney

2HarvardT.H.ChanSchoolofPublicHealth3AustralianRedCross

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Thistalkwillfocusonchallengesthatarisewhenfacedwiththeanalysisofdatasetsthataretoolargeforstandardstatisticalmethodstoworkproperly.Whileonecanalwaysgofortheexpensivesolutionofgettingaccesstoamorepowerfulcomputerorcluster,itturnsoutthattherearesomesimplestatisticalstrategiesthatcanbeused.Inparticular,we'lldiscusstheuseofsocalled"DivideandRecombine"strategiesthatrelegatesomeoftheworktobedoneinadistributedfashion,forexampleviaHadoop.Combiningthesestrategieswithcleversubsamplinganddatacoarseningideascanresultindatasetsthataresmallenoughtomanageonastandarddesktopmachine,withonlyminimalefficiencyloss.TheideasareillustratedwithdatafromtheAustralianRedCross.

Wednesday29th14:30Narrabeen

OptimalExperimentalDesignForFunctionalResponseExperiments

JeffZhangandChristopherDrovandiQueenslandUniversityofTechnology

Functionalresponsemodelsareimportantinunderstandingpredator-preyinteractions.Thedevelopmentoffunctionalresponsemethodologyhasprogressedfrommechanisticmodelstomorestatisticallymotivatedmodelsthatcanaccountforvarianceandtheover-dispersioncommonlyseeninthedatasetscollectedfromfunctionalresponseexperiments.However,littleinformationseemstobeavailabletothosewishingtoprepareoptimalparameterestimationdesignsforfunctionalresponseexperiments.Wedevelopaso-calledexchangedesignoptimisationalgorithmsuitableforinteger-valueddesignspaces,whichforthemotivatingfunctionalresponseexperimentinvolvesselectingthenumberofpreyusedforeachobservation.Further,wedevelopandcomparenewutilityfunctionsforperformingrobustoptimaldesigninthepresenceofparameteruncertainty,whicharegenerallyapplicable.Themethodsareillustratedusingapublishedbeta-binomialfunctionalresponsemodelforanexperimentinvolvingthefreshwaterpredatorNotonectaglauca(anaquaticinsect)preyingonAsellusaquaticus(asmallcrustacean)asacasestudy.

Wednesday29th14:30Gunnamatta

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ToPCAOrNotToPCA

CatherineM.McKenzie1,WeiZhang1,StuartD.Card1,CoryMatthew2,WadeJ.Mace1,andSivaGanesh1

1AgResearch2MasseyUniversity

Whentherearegroupingsofobservationspresentinthedata,manyresearchersresorttoutilisingaPrincipalComponentsAnalysis(PCA)aprioriforidentifyingpatternsinthedata,andthenlooktomapthepatternsobtainedfromPCAtodifferencesamongthegroupings,orattributebiologicalsignaltothem.Isthisappropriate,giventhatPCA’sarenotdesignedtodiscriminatebetweenthegroupings?Isagroup-orientedmultivariatemethodologysuchasmultivariateanalysesofvariance(MANOVA)orCanonicalDiscriminantAnalysis(CDA)preferable?Whichmethodhasmorerelevancewheninvestigatingfactoreffectsofbiochemicalpathways?Weexplorethisquestionviaabiologicalexample.

Twobiologicalmaterials(B1&B2)wereanalysedforthesame19primarymetabolites,withthreefactorsofMethods(M1&M2),Treatment(T1,T2,T3andT4),andAge(A1&A2),withthreereplicatevaluesgivingatotalof48observationsforeachbiologicalmaterial.Univariateandmultivariateanalysesofvariance(ANOVAandMANOVA,respectively)werecarriedout,forwhichthereweremanystatisticallysignificantinteractioneffects.Inaddition,othermultivariatetechniquessuchasPCAandCDAwereusedtoexplorerelationshipsbetweenthevariables.ThequestionremainsastotheappropriatenessofcarryingoutPCAtoexplorebiochemicalpathways,thecomparisonbeingbetweentailoringthepatternextractionaprioritomatchtheknowngroupingswithinthedataversusstartingwithanunrestrainedpatternanalysisandseekingtoexplainthepatternsdetectedpost-analysis?

Wednesday29th14:50Narrabeen

AnEvaluationOfErrorVarianceBiasInSpatialDesigns

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EmlynWilliams1andHans-PeterPiepho21ANU

2UniversityofHohenheim

Spatialdesignandanalysisarewidelyused,particularlyinfieldexperimentation.However,itisoftenthecasethatspatialanalysisdoesnotenhancemoretraditionalapproachessuchasrow-columnanalysis.Itisthenofinteresttogaugethedegreeoferrorvariancebiasthataccrueswhenaspatially-designedexperimentisanalysedasarow-columndesign.ThistalkbuildsontheworkofTedin(1931)who,withR.A.Fisherasadvisor,studiederrorvariancebiasinknight'smoveLatinsquares.

Wednesday29th14:50Gunnamatta

NewModel-BasedOrdinationDataExplorationToolsForMicrobiomeStudies

OlivierThas1,StijnHawinkel1,andLucBijnens21GhentUniversity

2JanssenPharmaceutics

High-throughputsequencingtechnologiesalloweasycharacterizationofthehumanmicrobiome,butthestatisticalmethodsforanalysingmicrobiomedataarestillintheirinfancy.DataexplorationoftenreliesonclassicaldimensionreductionmethodssuchasPrincipalCoordinateAnalysis(PCoA),whichisbasicallyaMultidimensionalScaling(MDS)methodstartingfromecologicallyrelevantdistancemeasuresbetweenthevectorsofrelativeabundancesofthemicroorganisms(e.gBray-Curtisdistance).

Wewilldemonstratethattheseclassicalvisualisationmethodsfailtodealwithmicrobiome-specificissuessuchasvariabilityduetolibrary-sizedifferencesandoverdispersion.Nextweproposeanewtechniquethatisbasedonanegativebinomialregressionmodelwithlog-link,andwhichreliesontheconnectionbetweencorrespondenceanalysisandthelog-linearRC(M)modelsofGoodman(AnnalsofStatistics,vol.13,1985);seealsoZhuetal.(EcologicalModelling,vol.187,2005).InsteadofassumingaPoissondistributionforthecounts,anegativebinomialdistributionisassumed.Tobetteraccountforlibrarysize

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effects,weadoptadifferentweightingscheme,whichnaturallyarisesfromtheparameterisationofthemodel.AniterativeparameterestimationmethodisproposedandimplementedintoR.Thenewmethodisillustratedonseveralexampledatasets,anditisempiricallyevaluatedinasimulationstudy.Itisconcludedthatourmethodsucceedsbetterindiscoveringstructureinmicrobiomedatasetsthanwithotherconventionalmethods.

Inthesecondpartofthepresentationweextentthemodel-basedmethodtoaconstrainedordinationmethodbyusingsample-specificcovariatedata.Themethodlooksforatwo-dimensionalvisualisationthatoptimallydiscriminatesbetweenspecieswithrespecttotheirsensitivitytoenvironmentalconditions.AgainwebuilduponresultsofZhuetal.(2005)andZhangandThas(StatisticalModelling,vol.12,2012).Themethodisillustratedonrealdata.

AllmethodsareavailableasanRpackage.

Wednesday29th15:10Narrabeen

AlwaysRandomize?

ChrisBrien1,21UniversityofSouthAustralia

2UniverstiyofAdelaide

Fishergaveusthreefundamentalprinciplesfordesignedexperiments:replication,randomizationandlocalcontrol.Consonantwiththis,Brienetal.(2011)[Brien,C.J.,Harch,B.D.,Correll,R.L.,&Bailey,R.A.(2011)Multiphaseexperimentswithatleastonelaterlaboratoryphase.I.Orthogonaldesigns.JournalofAgricultural,Biological,andEnvironmentalStatistics,16,422-450.]exhorttheuseofrandomizationinmultiphaseexperimentsviatheirPrinciple7(Allocateandrandomizeinthelaboratory).Thisprincipleisqualifiedwith‘whereverpossible’,whichleadstothequestion‘whenisrandomizationnotpossible?’.

Situationswhererandomizationisnotapplicablewillbedescribedforbothsingle-phaseandmultiphaseexperiments.Thereasonsfornotrandomizingincludepracticallimitationsand,formultiphaseexperiments,difficultyinestimatingvarianceparameterswhenrandomizationisemployed.Forthelatter

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case,simulationstudiescanvassinganumberofpotentialdifficultieswillbedescribed.ANonrandomizationPrinciple,andanaccompanyinganalysisstrategy,formultiphaseexperimentswillbeproposed.

Wednesday29th15:10Gunnamatta

ComparingClassicalCriteriaForSelectingIntra-ClassCorrelatedFeaturesForThree-ModeThree-WayData

LynetteHunt1andKayeBasford21UniversityofWaikato

2UniversityofQueensland

Manyunsupervisedlearningtasksinvolvedatasetswithbothcontinuousandcategoricalattributes.Onepossibleapproachtoclusteringsuchdataistoassumethatthedatatobeclusteredcomefromafinitemixtureofpopulations.Therehasbeenextensiveuseofmixtureswherethecomponentdistributionsaremultivariatenormalandwherethedatawouldbedescribedastwomodetwowaydata.Thefinitemixturemodelcanalsobeusedtoclusterthreewaydata.Themixturemodelapproachrequiresthespecificationofthenumberofcomponentstobefittedtothemodelandtheformofthedensityfunctionsoftheunderlyingcomponents.

Thistalkillustratestheperformanceofseveralcommonlyusedmodelselectioncriteriainselectingboththenumberofcomponentsandtheformofthecorrelationstructureamongsttheattributeswhenfittingamixturemodeltothefinitemixturemodeltoclusterthreewaydatacontainingmixedcategoricalandcontinuousattributes

Wednesday29th15:50Narrabeen

ExploringTheSocialRelationshipsOfDairyGoats

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VanessaCave1,BenjaminFernoit2,JimWebster1,andGosiaZobel11AgResearch

2AgrosupDijon

Goatsaresentientbeingscapableofanemotionalresponsetotheirlives.Yetdespitethis,thesocialrelationshipsbetweenanimalsarelargelyoverlookedincommercialsystems.Goatshavebeenshowntorecognise,andmakedecisionsbasedon,thepresenceofotherspecificgoats.Thedegreetowhichtheychoosetoassociatewithindividualshasnotbeenestablished,butsocialbondsinotheranimalshavebeenshowntobufferagainststressfulsituations,andconverselycanbeasignificantsourceofstresswhensuchbondsaredisrupted.

Toinvestigatewhethersocialrelationshipsexistamongdairygoats,4non-consecutivedaysofvideofocusingonagroupof12goatswasanalysed.Atoneminutescanintervals,theproximityofeverygoatrelativetoeveryothergoatwasrecordedasanordinalvariablewithfourlevels(incontact,withinaheadlength,withinabodylength,oralone).Eachgoat’slocationinthepenwasalsonoted(feeding,bedding,climbingplatform).

Avarietyofstatisticaltechniques,includingheatmapsandnetworkanalyses,wereusedtostudythesocialrelationshipsamonggoatsbasedonproximity.Socialrelationshipswerecharacterisedbyspecificpairsofgoatsreliablyspendingalotoftimeincloseproximity.

Resultsindicatethatwhilstsomegoatswere“sociable”(e.g.,spendingmorethan60%oftheirtimewithothergoats),otherstendedtobe“loners”(e.g.,spendingmorethan60%oftheirtimealone).Interestingly,therewasevidenceofbothpreferredandavoidedcompanionships.

Thissmall-scalestudyprovidesthefirstevidencetosuggestthatcommonmanagementpracticesresultingintheregroupingofdairygoatscouldhaveanimpactontheirwelfare.

Wednesday29th15:50Gunnamatta

BayesianSemi-ParametricSpectralDensityEstimationWithApplicationsToThe

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SouthernOscillationIndex

ClaudiaKirch1,MattEdwards2,AlexanderMeier1,andRenateMeyer21UniversityofMagdeburg2UniversityofAuckland

StandardtimeseriesmodellingisdominatedbyparametricmodelslikeARMAandGARCHmodels.EventhoughnonparametricBayesianinferencehasbeenarapidlygrowingareaoverthelastdecade,onlyveryfewnonparametricBayesianapproachestotimeseriesanalysishavebeendeveloped.Mostnotably,CarterandKohn(1997),Gangopadhyay(1998),Choudhurietal.(2004),andRosenetal(2012)usedWhittle'slikelihoodforBayesianmodelingofthespectraldensityasthemainnonparametriccharacteristicofstationarytimeseries.Ontheotherhand,frequentisttimeseriesanalysesareoftenbasedonnonparametrictechniquesencompassingamultitudeofbootstrapmethods(KreissandLahiri,2011,KirchandPolitis,2011).

AsshowninContreras-Cristanetal.(2006),thelossofefficiencyofthenonparametricapproachusingWhittle'slikelihoodapproximationcanbesubstantial.Ontheotherhand,parametricmethodsaremorepowerfulthannonparametricmethodsiftheobservedtimeseriesisclosetotheconsideredmodelclassbutfailifthemodelismisspecified.Therefore,wesuggestanonparametriccorrectionofaparametriclikelihoodthattakesadvantageoftheefficiencyofparametricmodelswhilemitigatingsensitivitiesthroughanonparametricamendment.WeuseanonparametricBernsteinpolynomialprioronthespectraldensitywithweightsinducedbyaDirichletprocess.ContiguityandposteriorconsistencyforGaussianstationarytimeserieshavebeenshowninapreprintbyKirchetal(2017).BayesianposteriorcomputationsareimplementedviaaMH-within-Gibbssamplerandtheperformanceofthenonparametricallycorrectedlikelihoodisillustratedinasimulation.WeusethisapproachtoanalysethemonthlytimeseriesoftheSouthernOscillationIndex,oneofthekeyatmosphericindicesforgaugingthestrengthofElNinoeventsandtheirpotentialimpactsontheAustralianregion.

Wednesday29th16:10Narrabeen

EfficientMultivariateSensitivityAnalysisOf

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AgriculturalSimulators

DanielGladishCSIRO

Complexmechanisticcomputermodelsoftenproducemultivariateoutput.Sensitivityanalysiscanbeusedtohelpunderstandsourcesofuncertaintyinthesystem.Muchoftheliteraturearoundsensitivityanalysishasfocusedonunivariateoutput,withsomerecentadvancesusingmultivariatecorrelatedoutput.Onepromisingmethodformultivariatesensitivityanalysisinvolvesdecompositionthroughbasisfunctionexpansion.However,thesemethodsoftenrequireseveralmodelrunsandmaystillbecomputationallyintensiveforpracticalpurposes.Emulatorshavebeenaprovenmethodforreducingcomputationaltimeforunivariatesensitivityanalysis,withsomerecentdevelopmentformultivariatecomputermodels.Weproposetheuseofgeneralizedadditivemodelsandrandomforestscombinedwithaprincipalcomponentanalysisforemulationforamultivariatesensitivityanalysis.Wedemonstrateourmethodusingacomplexagriculturalsimulator.

Wednesday29th16:10Gunnamatta

BayesianHypothesisTestsWithDiffusePriors:CanWeHaveOurCakeAndEatItToo?

JohnOrmerod,MichaelStewart,WeichangYu,andSarahRomanesUniversityofSydney

WeintroduceanewclassofpriorsforBayesianhypothesistesting,whichwename"cakepriors".ThesepriorscircumventBartlett'sparadox(alsocalledtheJeffreys-Lindleyparadox);theproblemassociatedwiththeuseofdiffusepriorsleadingtononsensicalstatisticalinferences.Cakepriorsallowtheuseofdiffusepriors(havingonescake)whileachievingtheoreticallyjustifiedinferences(eatingittoo).WedemonstratethismethodologyforBayesianhypothesestestsforscenariosunderwhichtheoneandtwosample -tests,andlinearmodelsaretypicallyderived.Theresultingteststatisticstaketheformofapenalized

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likelihoodratioteststatistic.ByconsideringthesamplingdistributionunderthenullandalternativehypothesesweshowforindependentidenticallydistributedregularparametricmodelsthatBayesianhypothesistestsusingcakepriorsarestronglyChernoff-consistent,i.e.,achievezerotypeIandIIerrorsasymptotically.Lindley'sparadoxisalsodiscussed.

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ProgrammeAndAbstractsForThursday30thOfNovember

Keynote:Thursday30th9:00Mantra

AGeneralFrameworkForFunctionalRegressionModelling

SonjaGrevenandFabianScheiplLMUMunich

Recenttechnologicaladvancesgenerateanincreasingamountoffunctionaldata,datawhereeachobservationrepresentsacurveoranimage(RamsayandSilverman,2005).Examplesoftechnologiesthatgeneratefunctionaldataincludeimagingtechniques,accelerometers,spectroscopyandspectrometry.Anykindofmeasurementcollectedovertime-datausuallyreferredtoaslongitudinal-canalsobeviewedaspotentiallysparselyobservedfunctionaldata.

Researchersareincreasinglyinterestedinregressionmodelsforfunctionaldatatorelatefunctionalobservationstoothervariablesofinterest.Wewilldiscussacomprehensiveframeworkforadditive(mixed)modelsforfunctionalresponsesand/orfunctionalcovariates.Theguidingprincipleistoreframefunctionalregressionintermsofcorrespondingmodelsforscalardata,allowingtheadaptationofalargebodyofexistingmethodsforthesenoveltasks.Theframeworkencompassesmanyexistingaswellasnewmodels.Itincludesregressionfor'generalized'functionaldata,meanregression,quantileregressionaswellasgeneralizedadditivemodelsforlocation,shapeandscale(GAMLSS)forfunctionaldata.Itadmitsmanyflexiblelinear,smoothorinteractiontermsofscalarandfunctionalcovariatesaswellas(functional)randomeffectsandallowsflexiblechoicesofbases-inparticularsplinesandfunctionalprincipalcomponents-andcorrespondingpenaltiesforeachterm.Itcoversfunctionaldataobservedoncommon(dense)orcurve-specific(sparse)grids.Penalized

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likelihoodbasedandgradient-boostingbasedinferenceforthesemodelsareimplementedinRpackagesrefundandFDboost,respectively.Wealsodiscussidentifiabilityandcomputationalcomplexityforthefunctionalregressionmodelscovered.Arunningexampleonalongitudinalmultiplesclerosisimagingstudyservestoillustratetheflexibilityandutilityoftheproposedmodelclass.ReproduciblecodeforthiscasestudyisalsoavailableonlinewiththerecentdiscussionpaperGrevenandScheipl(2017)thistalkisbasedon.

Thursday30th10:30Narrabeen

HockeySticksAndBrokenSticks–ADesignForASingle-Arm,Placebo-Controlled,Double-Blind,RandomizedClinicalTrialSuitableForChronicDiseases

HansHockey1andKristianBrock21BiometricMattersLtd.

2CancerResearchUKClinicalTrialsUnit

Thisworkismotivatedandexemplifiedbyageneticdisordercausingearlyonsetdiabetes,blindnessanddeafness,whichisextremelyrare,inevitablyfatalandhasnocurrentdirecttreatment.Whilethestandardplacebo-controlledRCTisthegoldstandardrequiredbytheregulatoryagencyforanewproposeddrugstudy,itisconjecturedthatpotentialstudyparticipantswillpreferadesignwhichguaranteesthattheyarealwaysassignedtothedrugunderstudy.Asingle-armdesignisproposedwhichmeetsthispatientneedandhenceprobablyincreasesrecruitmentandcompliance.Atthesametime,itmeetstherequirementforfullrandomization.Analyseswhichfollownaturallyfromthisdesignarealsodescribedandwereusedintrialsimulationsforsamplesizingandforexaminationoftheeffectofunderlyingassumptions.

Thursday30th10:30Gunnamatta

BoundingIVEstimatesUsingMediation

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AnalysisThinking

TheisLange1,21UniversityofCopenhagen

2PekingUniversity

Inthispaperweinitiallydemonstratethatthewell-knownassumptionsforconductingIV-analysescanequivalentlybeexpressedusingnaturaleffectsfrommediationanalysis.Viewingtheassumptions,anindeedthewholeIVanalysis,fromamediationanalysisperspectiveopensupanovelpossibilityforboundingthetruecausaleffectoftheexposurewhenthedistributionalassumptionsoftheIVanalysisfail;e.g.ifthereisaninteractionsbetweentheunmeasuredconfoundersandexposure.Theprocedureworksacrossalleffectscales(riskdifference,hazardratioetc.)andtypesofviolationsofthedistributionalassumptions.TheproposedmethodcanalsobeviewedasasensitivityanalysisfortheIV-analysiswherethereisonlyasingletuningparameter,whichcanbestraightforwardlyinterpreted.ForthepurelybinarycasetheproposedboundsconvergestotheBalkeandPearlboundswhenthetuningparametertendstoinfinity(i.e.wheneventhemostextrememisspecificationisconsidered).Astheproposedmethodiscomputationallydemandingsometimeisspentonimplementationconsiderations.

Thursday30th10:50Narrabeen

CorrelatedBivariateNormalCompetingRisks---StructuringEstimationInAnIll-PosedProblem

MalcolmHudsonMacquarieUniversity

Estimationofcorrelationbetweencompetingriskshaslongbeenknowntobeanill-posedproblem,duetolackofidentifiablity,termedtheidentifiabilitycrisis[Crowder,ScandJStat1991].Recently,manysemi-parametricmodelsandafullyparametricmodelhavebeenusedtopermitestimationandassess

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sensitivityofresultstodegreeofcorrelation[JeongandFineBiostatistics2007;seealsoTaietal,SIM2008].Parametricmodelsarenon-standardandcalculationscomplex;JeongandFine'sparametricmodelemploysaGompertzdistribution.Forlog-Normaldata,complexityofcalculationsinabivariateNormal(BVN)modelisforbiddinganddirectoptimizationunstable.

WedescribeaparametricsolutionavailableintheBVNcase.OurapproachusesanEMalgorithmforcompetingrisks(generalisingtheAitkin'sEMforunivariatesurvival).ComplexcalculationsareevaluatedinclosedformusingalemmaofStein[Liu,StatistProbLetters1994].Thisprobabilisticandstatisticalplatformforexploringtheill-posednessisimplementedwithinthebncR-package(inpreparation).

Weintroducethesevariouscomponentsinthetalk.

Thursday30th10:50Gunnamatta

BayesianRegressionWithFunctionalInequalityConstraints

JoshuaBon1,BerwinTurlach1,KevinMurray1,andChristopherDrovandi21UniversityofWesternAustralia

2QueenslandUniversityofTechnology

WeinvestigatehowtoconductBayesianinferencewithfunctionalinequalityconstraintsovertheparameterspace.Thisproblemisanalogoustosemi-infiniteprogramminginoptimisation.AnovelmethodusingSequentialMonteCarlo(SMC)isgiven.TheSMCalgorithmapproximatesthedistributionofminimainordertosuccessivelyiteratetowardstheconstrainedparameterspace.Wedemonstrateonforensicmorphometricdataofhumanskullswheremonotonicityisrequiredinmorethanonedimension.Methodsarecomparedtothosecurrentlyavailablewithonedimensionalconstraintsandtheresultsdiscussed.

Thursday30th11:10Narrabeen

GeneticAnalysisOfRenalFunctionInAn

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IsolatedAustralianIndigenousCommunity

RussellThomson1,BrendanMcMorran2,WendyHoy3,MatthewJose4,TimThornton5,GaétanBurgio2,andSimonFoote2

1WesternSydneyUniversity2ANU

3UniversityofQueensland4UniversityofTasmania

5UniversityofWashington

Incloseconsultationwiththelocallandcouncil,andwithethicalapprovalfrommanyethicscommittees,wehaveperformedagenome-wideassociationstudy(GWAS)onasamplecohortfromthe1990s.Wealsohaveafollowupdatasetof120studyparticipantsfrom2014,withDNAsequencedata.

Iwilldiscussthestatisticalanalysesofthesedatasets,withrespecttoissuesofdegradedDNA,higherrorratesandcorrelatedsamples.

Thursday30th11:10Gunnamatta

ThePerformanceOfModelAveragedTailAreaConfidenceIntervals

PaulKabailaLaTrobeUniversity

Commonlyinappliedstatistics,thereissomeuncertaintyastowhichexplanatoryvariablesshouldbeincludedinthemodel.Frequentistmodelaveraginghasbeenproposedasamethodforproperlyincorporatingthis"modeluncertainty"intoconfidenceintervalconstruction.Suchproposalshavebeenofparticularinterestinenvironmentalandecologicalstatistics.

Theearliestapproachtotheconstructionoffrequentistmodelaveragedconfidenceintervalswastofirstconstructamodelaveragedestimatoroftheparameterofinterestconsistingofadata-basedweightedaverageoftheestimatorsofthisparameterunderthevariousmodelsconsidered.Themodel

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averagedconfidenceintervaliscenteredonthisestimatorandhaswidthproportionaltoanestimateofthestandarddeviationofthisestimator.However,thedistributionalassumptiononwhichthisconfidenceintervalisbasedhasbeenshowntobecompletelyincorrectinlargesamples.

AnimportantconceptualadvancewasmadebyFletcher&Turek(2011)andTurek&Fletcher(2012)whoputforwardtheideaofusingdata-basedweightedaveragesacrossthemodelsconsideredofproceduresforconstructingconfidenceintervals.Inthiswaythemodelaveragedconfidenceintervalisconstructedinasinglestep,ratherthanfirstconstructingamodelaveragedestimator.

WereviewtheworkofKabailaetal(2016,2017)whichevaluatestheperformanceofthemodelaveragedtailareaconfidenceintervalofTurek&Fletcher(2012)inthe“testscenario”oftwonestednormallinearregressionmodels.Ourassessmentofthisconfidenceintervalisthatitperformsquitewellinthisscenario,providedthatthedata-basedweightfunctioniscarefullychosen.

References:

1. Kabaila,P.,Welsh,A.H.,&Abeysekera,W.(2016)Model-averagedconfidenceintervals.ScandinavianJournalofStatistics.

2. Kabaila,P.,Welsh,A.H.andMainzer,R.(2017)Theperformanceofmodelaveragedtailareaconfidenceintervals.CommunicationsinStatistics-TheoryandMethods.

Thursday30th11:30Narrabeen

DeconstructingTheInnateImmuneComponentOfAMolecularNetworkOfTheAgingFrontalCortex

EllisPatrick1,MarikoTaga2,MartaOlah2,Hans-UlrichKlein2,CharlesWhite3,JulieSchneider4,LoriChibnik5,DavidBennett4,SaraMostafavi6,

ElizabethBradshaw2,andPhilipDeJager21UniversityofSydney2ColumbiaUniversity

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3TheBroadInstitute4RushUniversity

5HarvardUniversity6UniverstiyofBritishColumbia

Alzheimer'sdiseaseispathologicallycharacterizedbytheaccumulationofneuritic-amyloidplaquesandneurofibrillarytanglesinthebrainandclinicallyassociatedwithalossofcognitivefunction.ThedysfunctionofmicrogliacellshasbeenproposedasoneofthemanycellularmechanismsthatcanleadtoanincreaseinAlzheimer'sdiseasepathology.InvestigatingthemolecularunderpinningsofmicrogliafunctioncouldhelpisolatethecausesofdysfunctionwhilealsoprovidingcontextforbroadergeneexpressionchangesalreadyobservedinmRNAprofilesofthehumancortex.

WehaveusedmRNAsequencingtoconstructgeneexpressionprofilesofmicrogliapurifiedfromthecortexof11subjectsfromalongitudinalcohortofaging,RushMemoryAgingProject(MAP).Bystudyingthesemicrogliageneexpressionprofilesinthecontextoftissue-levelprofilesofthecortexof542subjectsfromtheMAPandReligiousOrdersStudy(ROS)weaddressdualproblems.ByusinginformationfromthelargeROSMAPcohort,weareabletoisolatethegeneswhicharestronglyassociatedwithimmuneresponse.Conversely,weillustratethatthemicrogliasignaturecanbeusedtohighlightpredefinedsetsofcoexpressedgenesinROSMAPthatarehighlyenrichedformicrogliagenes.AddressingthesetwoquestionsallowsustoidentifysetsofmicrogliaspecificgeneswhichareassociatedwithvariousAlzheimer'sdiseasetraitsfurtheremphasizingthemolecularconsequencesofmicrogliadysfunctioninthisdisease.Specifically,weareabletoidentifyasetofmicrogliarelatedgenesassociatedwithtauandamyloidpathologyaswellasanactivatedmicroglialmorphology.

Thursday30th11:30Gunnamatta

BiasCorrectionInEstimatingProportionsByPooledTesting

GrahamHepworth1andBradBiggerstaff2

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1UniversityofMelbourne2CentersforDiseaseControlandPrevention

Pooledtesting(orgrouptesting)ariseswhenunitsarepooledtogetherandtestedasagroupforthepresenceofanattribute,suchasadisease.Wehaveencounteredpooledtestingproblemsinplantdiseaseassessmentandprevalenceestimationofmosquito-borneviruses.

Intheestimationofproportionsbypooledtesting,theMLEisbiased,andseveralmethodsofcorrectingthebiashavebeenpresentedinpreviousstudies.WeproposeanewestimatorbasedonthebiascorrectionmethodintroducedbyFirth(1993),whichusesamodificationofthescorefunction.Ourproposedestimatorisalmostunbiasedacrossarangeofproblems,andsuperiortoexistingmethods.WeshowthatforequalpoolsizesthenewestimatorisequivalenttotheestimatorproposedbyBurrows(1987),whichhasbeenusedbymanypractitioners.

Thursday30th11:50Narrabeen

TheParametricCureFractionModelOfOvarianCancer

SerifatFolorunso,AngelaChukwu,andAkintundeOdukogbeUniversityofIbadan

WeproposeincorporatingtheGammalinkfunctiontothegeneralizedGammausingamixturecurefractionmodel.Themathematicalpropertiesoftheproposedmodelwereexploredandtheinferencesforthemodelswereobtained.TheproposedmodelcalledGamma-generalizedgammamixturecuremodel(GGGMCM)willbevalidatedusingareallifedatasetonovariancancer.

Thursday30th11:50Gunnamatta

TheSkillings-MackStatisticForRanksDataInBlocks

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JohnBestUniversityofNewcastle

SkillingsandMackgaveastatisticfortestingtreatmentdifferencesforranksdatainblocks-bothcompleteandincompleteblocksorblockswithmissingvalues.Thisgeneralstatisticisthusquiteuseful.ThereisanRpackageforitscalculation.Howeverweillustrateaproblemwithtiedranks.Sensoryevaluationdataisused.

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PosterAbstracts

Multi-EnvironmentTrialAnalysisOfAgronomyTrialsUsingEstablishedPlantPopulationDensity

MichaelMumfordandKerryBellQueenslandDepartmentofAgricultureandFisheries

Thereisagrowinginterestwithinthegrainsindustrytodeterminetheyieldresponsetoplantpopulationdensity.Thiscanvarydependingonthetriallocation,thehybridthatisplantedandthemanagementpracticesthatareemployed.

Inthepast,theanalysisofplantpopulationdensityhasbeenperformedusingthetargetedplantpopulationdensityasdeterminedbythenumberofseedsinitiallyplanted.However,ithasbeenfoundthatthetargetedplantpopulationdensitymaydiffersignificantlyfromtheestablishedplantpopulationdensity,i.e.theplantpopulationdensityobservedinthefield.Insuchcircumstances,itbecomesimportanttousetheestablishedratherthantargetedplantpopulationdensitiesinordertoprovidethemostinformativemeasureofyieldresponsetoplantdensity.Thisisespeciallytruewhenperformingamulti-environmenttrial(MET)analysisduetovariationbetweenthetargetedandestablishedplantpopulationdensityateachtrial.

WeproposeaprocedureforperformingaMETanalysisusingestablishedplantpopulationdensityasanexplanatoryvariable.Usingalinearmixedmodelframework,separateregressionlines/curvesarefittoeachcombinationoftrialandmanagementpractice,whichwedefineas‘environment’.Environmentsarethengroupedaccordingtotheresultsofaclusteranalysisperformedusingtheestimatesoftheregressioncoefficients,ensuringthatenvironmentswithinaclustershareasimilarresponsetoplantdensity.Thelines/curvesfittedtoenvironmentswithinclustersarethenparallel,allowingustothenperformFisher’sleastsignificantdifferencetestingforyielddifferencesbetween

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environmentswithineachcluster.

ThispresentationwillfocusontheapplicationoftheproposedmethodtoalargesetofmaizeandsorghumagronomytrialsconductedacrossNewSouthWalesandQueenslandoverthelastthreeyears.Forsimplicity,aseparateanalysiswasperformedforeachhybrid.

AdventuresInDigitalAgricultureInNewZealand

EstherMeenkenandVanessaCaveAgResearch

NewZealandisanationforwhichprimaryindustriesaretheeconomicbackbone.NewZealand'shighlyvariablelandscapemeansthereareawiderangeinclimaticandsoilconditions,withmultiplelandusesexistinginrelativelysmallspatialareas.ThismeanstherearemanypossibilitiestobeexploredinDigitalAgricultureasadvancesinsensorytechnologiesaremade.Thetechnicalchallengesknowntoberelatedtotheuseofsensorsincludeissuesaroundbigdata,missingdata,biasedandvariablesensors,andvarioustypesofstructuredandnon-normaldata.WewilldescribeasuiteofstudiesthathavebeenrecentlyundertakenthatcanbelooselygroupedundertheDigitalAgricultureumbrella.Casestudiesexplorearangeofscientificandstatisticalchallengesintheprimaryindustriesarena,andincludetheuseofclassicalandBayesianPrincipledExperimentalDesign,HierarchicalBayesianModels,neuralnetclassifiers,visualisation,animaltrackingviaGPS,anddatamanagement.

ModellingCanopyGreennessOverTimeUsingSplinesAndNon-LinearRegression

BethanyMacdonald1,JackChristopher2,andAlisonKelly11QueenslandDepartmentofAgricultureandFisheries

2QueenslandAllianceforAgricultureandFood

Theabilityofaplanttoretainleafgreennessforanextendedtimeafteranthesis,

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knownasstay-green,hasbeenlinkedtogreateryieldinwheatandcanvaryamongstgenotypes.Manyaspectsofaplant'ssenescencecontributetostay-green;therefore,accuratelymodellingsenescenceandunderstandingthegeneticvariationinsenescencedynamicsisintegraltoselectingforthestay-greenphenotype.Normaliseddifferencevegetativeindex(NDVI)providesameasureofcanopygreennessandwhencollectedovertime,cangiveanindicationofwhensenescencebeginsandtherateatwhichitcontinues.Christopheretal.(2014)capturedgeneticvariationinsenescencedynamicsusingmultiplestay-greentraitsderivedfromalogisticregressionoflongitudinalNDVImeasurements.Thesetraitswereestimatedindependentlyforeachplotwithintheexperiment,ignoringanysourcesofcovariance,andthensubsequentlyanalysedusingalinearmixedmodel.However,noestimationerrorswerecarriedfromthefirsttothesecondstageoftheanalysis.

Wediscusstwoalternativeone-stagemethodsformodellingsenescencedynamicsbasedonlongitudinalNDVImeasurements.Bothofthesemethodsenablethevariabilityinsenescencepatternstobepartitionedintogeneticandnon-geneticcomponents,whilealsoincorporatingtheexperimentalstructure.Thefirstmethodinvolvestheuseofsplinesinalinearmixedmodelframework.Thismethodallowsanappropriatecovariancestructureforrepeatedmeasurestobeutilisedand,unlikeotherapproaches,enablesflexibilityinthesenescencepatterns.Thesecondmethodinvolvesfittinglogisticequationsinanon-linearmixedmodelframework.Theestimatedparametershavebiologicalinterpretations,aidinginthegeneticcomparisonofsenescencedynamics.Thesetwomethodsofferamorestatisticallyrobustapproachtomodellingcropsenescenceandtheunderlyinggeneticvariability.

OnTestingMarginalHomogeneityForSquareContingencyTablesWithOrdinalCategories

KoujiTahataTokyoUniversityofScience

Fortheanalysisofsquarecontingencytableswithorderedcategories,themarginalhomogeneitymodel,whichindicatestherowmarginaldistributionis

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equaltothecolumnmarginaldistribution,wereconsidered.Stuart(1955)proposedtheteststatistics(denotedbyQ)forthemarginalhomogeneitymodel.Howeveritdoesnotusetheinformationofthecategoryordering.Agresti(1983)comparedbetweenQandtheMann-Whitneytestaboutthepowerbysimulations.Inthispaper,weconsidersomemeasurestorepresentthedegreeofdeparturefromthemarginalhomogeneity.Forexample,Tomizawa,MiyamotoandAshihara(2003)andTahata,IwashitaandTomizawa(2008).InthesituationgivenbyAgresti(1983),wecomparebetweenQandtestbasedonthesemeasuresaboutthepower.

AFactorAnalyticApproachToModellingDiseaseProgressionAcrossLeafLayersAndTime

ClaytonForknall,GregPlatz,LisleSnyman,andAlisonKellyQueenslandDepartmentofAgricultureandFisheries

ControllingandlimitingtheimpactoffoliardiseasesisachallengefacedbytheAustraliangrainsindustry.Foliardiseasescompromiseanddestroyphotosyntheticarea,thuslimitingplantresourcesandadverselyaffectingcropproductivity.Suchdiseasesofteninfectthelowerleaflayersofplantsearlyintheseasonand,dependentontheircompatibilitywiththehostandenvironment,progresstowardsthetopmostleaflayersovertime.

Synthesizingthecomplicateddynamicsofdiseaseprogression,overdifferentleaflayersandacrossagrowingseason,intoameasuretoestimatetheimpactoflossofleafareaonproductivityrequirestheassessmentoftheproportionofleafareacompromisedbydisease(LAD)atmultipletimesthroughouttheseason.AsimplemeasurethatcapturesbothLADanddiseasedurationonagivenleaflayeristheAreaUndertheDiseaseProgressCurve(AUDPC),formedbyapplyingthetrapezoidruletoLADassessmentsovertime.TheAUDPCisthenoftencorrelatedtoameasureofproductivityforestimatingtheimpactofdiseaseateitheravarietyorexperimentalunitlevel.

WeproposeanalternativeapproachtotheAUDPCtomodeldiseaseprogressionovertimeandleaflayersmoreefficiently.Usingafactoranalyticapproach,thecovariancebetweenleaflayers,bothwithinandacrossassessmenttimes,is

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capturedonavarietybasisinalinearmixedmodelframework.Thisapproachwillnotonlyprovidemorereliableestimatesofthediseasepressureexertedonvarieties,butalsoinformofrelationshipsbetweenleaflayersandhowconsistenttheserelationshipsareacrosstime.Betterqualityinformationregardingtherelationshipbetweenlossofleafareaandproductivitycanbemadeavailabletoindustrythroughtheimprovedestimatesandunderstandingofdiseaseprogressionobtainedfromthismodellingapproach.

AssociatingStrawStrengthWithLikelihoodOfHeadLossInBarleyForWesternAustralia

DeanDiepeveen1,KefeiChen1,ChengdaoLi2,andDavidFarleigh31CurtinUniversity

2MurdochUniversity3DepartmentofPrimaryIndustriesandDevelopment

Barleyheadlossischaracterisedbystrawbreakagejustbelowtheheadatornearplantmaturity.Ourrecentresearchintobarleyheadlosshasenabledustoevaluatethevariouscomponentsofplantmaturityandhavefoundacleargeneticcomponenttothisindustryissue.Themorechallengingcomponentsofthisresearchistounderstandwhyourmoretolerantcultivarsalsoshowsubstantialheadlossincertainunseasonalconditionsduringharvest.Ourpreliminaryanalysesusesenvironmentaldatawithplantmeasurementdataandlooksattheincidencesofextremeweathereventsonbarleyheadloss.Whatconfoundstheanalysisisthenutrient/growthconditionsoftheplantandthepredispositionofthesebarleycropstoheadlossduringtheseweatherevents.Thispaperwillprovidesomelessonslearntandfuturedirections.

ADeepLearningNeuralNetworkModelToIdentifyTheImportantGenesInMetastaticBreastCancerFromCensoredMicroarrayData

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Quoc-AnhTrinhVietnamNationalUniversity

Microarraydatahavebeenshowntocorrelatedwithsurvivalinbreastcancer.Themostdifficultinanalysisofthisdataconsistinasimultaneousmeasureofhugegeneexpressionlevelsoverafewpatientsavailable.TheconventionalsurvivalmodelssuchastheproportionalhazardmodelofCoxarenomoreappropriatedincludingthehazardproportionalhypothesisandthelinearcovariateseffecthypothesis.Prognosticstudiesinvolvetheidentificationofgenesthatcorrelatewithsurvivalinordertoprovidenewinformationonpathogenesis.Thisresultmayaidintheresearchofdrugdesignfornewtargets.Weintroduceinthispaperadeeplearningrecurrentneuralnetworkapproachtopredictsurvivaltimesfortheindividualpatientbasedonmicroarraymeasurement.Thisneuralnetworkdefinesthehazardfunctionofsurvivalnottomentiontheproportionalityandlinearityhypothesis.Wepresentadeeplearningapproachtotheidentificationofgenescriticalforthemetastasesofbreastcancer.Weidentifiedasetofhighlyinteractivegenesbyanalysingtheconnectivitymatrices.

UsingRAndShinyToDevelopWebBasedSamplingApplicationsForTheAgriculturalAndEducationSectors

PeterKasprzak,OlenaKravchuk,andAndyTimminsUniveristyofAdelaide

Samplingisanimportantaspectofagriculturalstatisticsandanimportantfeatureofindustrialreality.Enablinganefficientdialoguewithresearchersandagronomistsaboutsoundmethodsforfielddatacollectionwillincreasetheefficiencyandreliabilityofagriculturalprojects.TheShinypackageinRallowsinteractivewebapplicationstobecreatedbaseduponexistingRpackages.ThispresentationwilloutlinethestepsinvolvedincreatinganappthatimplementssamplingtechniquessuchasSRS,StratifiedSampling,andRatioSampling,withinavisuallyappealinggraphicalenvironmentforuseintheeducationandagriculturalsectors.

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GraphicalNetworkAnalysesInformsBiomarkerDiscoveryViaQuantificationOfKeyDiseaseRelatedConnections

JamesDoeckeCSIRO

Determinationoftheoptimalsetofbiomarkersoftenreliesonlargescalefeatureselection,thatselectsaminimalsetofindependentbiomarkers,withoutconsiderationofanyinformationregardingthebiologicalnetwork.Thisresearchisaimedatdefiningbiologicalnetworkstructuresrelatedtodiseasephenotype,throughathree-levelstatisticalprocess.Briefly,wefirstimplementanunsupervisedcorrelationstructureacrossthesetofbiomarkers,usedtodefineclustersofbiomarkersviastrictthresholding.Next,usingthegraphicalnetworkanalysismethodDifferentialNetworkAnalysisinGenomics(DINGO),wedefinethedifferentialnetworkstructureforhealthyanddiseasestates.Lastlyfromthederiveddifferentialconnectivityscoreweclustertheindividualgroupsofbiomarkers.Fromthisprocess,weareabletoproduceanetworkofsimilarlygroupedsetsofbiomarkersthatcanseparatehealthyfromdiseasesubjects.Lastlyweperformarandomeffectsmodelforeachclustertoestimatetheassociationwithdiseasestatus.Usingthisdesign,wehavebeenabletoincorporateinformationfrommultiplecorrelatedbiomarkerstoincreasetheproportionofvarianceexplainedindiseasestatus.Wetestthisdesignontwodatasets,1)asetof~2000peptidebiomarkers,withtheexpressaimofdefiningsetsofbiomarkersassociatedwithAlzheimer’sDiseasepathology,and2)asetof~2000mRNAprobesetscombinedwithasetof~2000DNAmethylationprobeswiththeexpressaimofdefiningsetsofbiomarkersassociatedwithbreastcancer.Wepresentnetworkplotsandconnectivitystructuresforeachdataset,andhighlighttheincreasedperformanceofclusteredbiomarkersascomparedtoindividualbiomarkerstoseparatediseasestatus.Lastly,weshowthatbyutilizinganetworkapproach,wecanincorporatemorebiologicalinformationfromthedatatoexplainoutcome.

Spatio-TemporalCorticalBrainAtrophyPatternsOfAlzheimer'sDisease

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MarcelaCespedes1,JamesMcGree1,ChristopherDrovandi1,KerrieMengersen2,JamesDoecke3,andJurgenFripp3

1QueenslandUniversityofTechnology2QueenslandUniversityofTechnlogy

3CSIRO

Thedegenerationofthecerebralcortexisacomplexprocesswhichoftenspansdecades.Thisdegenerationcanbeevaluatedonregionsofinterest(ROI)inthebrainthroughprobabilisticnetworkanalysis.However,currentapproachesforfindingsuchnetworkshavethefollowinglimitations:1)analysisatdiscreteagegroupscannotappropriatelyaccountforconnectivitydynamicsovertime;and2)morphologicaltissuechangesareseldomunifiedwithnetworks,despiteknowndependencies.Toovercometheselimitations,aprobabilisticdynamicwombledmodelisproposedtosimultaneouslyestimateROIcorticalthicknessandnetworkcontinuouslyoverage,andwascomparedtoanageaggregatedmodel.Theinclusionofageinthenetworkmodelwasmotivatedbytheinterestininvestigatingthepointintimewhenconnectionsalteraswellasthelengthoftimerequiredforchangestooccur.Ourmethodwasvalidatedviaasimulationstudy,andappliedtohealthycontrols(HC)andclinicallydiagnosedAlzheimer'sdisease(AD)groups.Theprobabilityofalinkbetweenthemiddletemporal(akeyADregion)andtheposteriorcingulategyrusdecreasedfromage55(posteriorprobability>0.9),andwasabsentbyage70intheADnetwork(posteriorprobability<0.12).ThesameconnectionintheHCnetworkremainedpresentthroughoutages55to95(posteriorprobability≥0.75).Theanalysespresentedinthisworkwillhelppractitionerschoosesuitablestatisticalmethodstoidentifykeypointsintimewhenbraincovarianceconnectionschange,inadditiontomorphologicaltissueestimates,whichcouldpotentiallyallowformoretargetedtherapeuticinterventions.

EmpiricalModellingOfFruitFirmnessChangeDuringColourConditioning

LindyGuoandRingoFengPlantandFood

Gold3(Actinidia.chinensisZesy002,marketedasZespri®SunGoldKiwifruit)isanewyellow-fleshedkiwifruitcultivarbeingusedtoreplaceHort16Athathas

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beenalmostalldestroyedbytheoutbreakofPseudomonassyringaepv.actinidiae(Psa)in2010.TheSmartMonitoringprogrammestartedin2011,recordsfruitdevelopment,maturation,harvestingandstorageperformanceofGold3kiwifruitfrom12orchardsinafairlyconsistentmanneryearafteryear.

Theaimofthisprojectistosummarizefruitdevelopmentandstoragepotentialsoverthe5years,andhenceprovideabetterunderstandingoftherolesofseasonalweatherconditions,orchardmanagement,andfruitattributesatharvestaffectingstoragepotential.

Forthisproject,storagelifewasdefinedasstoragetimeforabatchoffruit(fruitharvestedfromanorchardonaparticulardate)tosoftento1kfg.Severalwaystocalculatedstoragelifebasedonfirmnessmonitoringdataarecomparedandthestorageliveswerecorrelatedwithweatherconditions,orchardmanagement,andfruitattributesatharvestusingdifferentalgorithms.Theresultswillbepresentedtohighlightexperienceslearntinthisdataanalysis.Recommendationswillbemadeondealingwithmultiyeardatawithcollinearvariables.


CoBi: Pattern Based Co-Regulated Biclustering of Gene ...jkalita/papers/2013/RoySwarupPatternRecognition... · CoBi: Pattern Based Co-Regulated Biclustering of Gene Expression Data

CoBi: Pattern Based Co-Regulated Biclustering of Gene ...jkalita/papers/2013/RoySwarupPatternRecognition... · CoBi: Pattern Based Co-Regulated Biclustering of Gene Expression Data

Tensor Biclustering - Neural Information Processing Systems · bicluster separately. This means that repeated applications of matrix biclustering cannot solve the tensor biclustering

Tensor Biclustering - Neural Information Processing Systems · bicluster separately. This means that repeated applications of matrix biclustering cannot solve the tensor biclustering

Analysis of Gene Expression and Gene Networks Biclustering 2.

Analysis of Gene Expression and Gene Networks Biclustering 2.

Biclustering Via Sparse Clustering - arXiv · Biclustering methods may be useful in situations where clusters are formed by only a subset of the features. Biclustering aims to identify

Biclustering Via Sparse Clustering - arXiv · Biclustering methods may be useful in situations where clusters are formed by only a subset of the features. Biclustering aims to identify

Biclustering-based imputation in longitudinal data · Biclustering-based imputation in longitudinal data Ines A. Nolasco, Alexandra M. Carvalho, Sara C. Madeiraˆ Abstract—Amyotrophic

Biclustering-based imputation in longitudinal data · Biclustering-based imputation in longitudinal data Ines A. Nolasco, Alexandra M. Carvalho, Sara C. Madeiraˆ Abstract—Amyotrophic

A new survey on biclustering of

A new survey on biclustering of

Consistent Biclustering via Fractional 0–1 Programmingmariog/slides/Pardalos_biclustering.pdf · Consistent Biclustering Conclusions Major Data Mining Problems Clustering (Unsupervised):

Consistent Biclustering via Fractional 0–1 Programmingmariog/slides/Pardalos_biclustering.pdf · Consistent Biclustering Conclusions Major Data Mining Problems Clustering (Unsupervised):

Application of Clustering and Biclustering Techniques to Yeast Metabolic … · 2017-09-21 · Application of Clustering and Biclustering Techniques to Yeast Metabolic Cycle Bioinform

Application of Clustering and Biclustering Techniques to Yeast Metabolic … · 2017-09-21 · Application of Clustering and Biclustering Techniques to Yeast Metabolic Cycle Bioinform

PREPROCESSING AND BICLUSTERING OF HIGH-THROUGHPUT ...bdmdotbe/bdm2013/documents/10-83(1).pdf · biclustering microarray datasets. We developed a novel biclustering framework, ProBic

PREPROCESSING AND BICLUSTERING OF HIGH-THROUGHPUT ...bdmdotbe/bdm2013/documents/10-83(1).pdf · biclustering microarray datasets. We developed a novel biclustering framework, ProBic

Biclustering of Gene Expression Data by Correlation-Based

Biclustering of Gene Expression Data by Correlation-Based

Biclustering: Methods, Software and Application · Biclustering: Methods, Software and Application Dissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften

Biclustering: Methods, Software and Application · Biclustering: Methods, Software and Application Dissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften

Bivariate Poisson and Diagonal Inflated Bivariate Poisson ... · Bivariate Poisson and Diagonal Inflated Bivariate Poisson Regression Models in R ... of the bivariate Poisson model.

Bivariate Poisson and Diagonal Inflated Bivariate Poisson ... · Bivariate Poisson and Diagonal Inflated Bivariate Poisson Regression Models in R ... of the bivariate Poisson model.

BICLUSTERING OF GENE EXPRESSION DATA - UMA

BICLUSTERING OF GENE EXPRESSION DATA - UMA

Clustering Algorithms for AI, CI, KE and Data Mininghome.agh.edu.pl/~horzyk/lectures/ci/AI-CI-KE Clustering Algorithms.pdf · Biclustering and Triclustering Biclustering •called

Clustering Algorithms for AI, CI, KE and Data Mininghome.agh.edu.pl/~horzyk/lectures/ci/AI-CI-KE Clustering Algorithms.pdf · Biclustering and Triclustering Biclustering •called

Compara’ve*Analysis*of*Biclustering* Algorithms*tda.gatech.edu › public › slides › Bozdag10-BCB.pdfDep. of Biomedical Informatics HPC Lab bmi.osu.edu/hpc Compara’ve*Analysis*of*Biclustering*

Compara’ve*Analysis*of*Biclustering* Algorithms*tda.gatech.edu › public › slides › Bozdag10-BCB.pdfDep. of Biomedical Informatics HPC Lab bmi.osu.edu/hpc Compara’ve*Analysis*of*Biclustering*

About OMICS Group · 2014-10-01 · About OMICS Group OMICS Group ... poster presentations, workshops, symposia and exhibitions. About OMICS Group Conferences OMICS Group International

About OMICS Group · 2014-10-01 · About OMICS Group OMICS Group ... poster presentations, workshops, symposia and exhibitions. About OMICS Group Conferences OMICS Group International

Biclustering Algorithms for Biological Data Analysis€¢ If all weights are binary, biclustering becomes biclique finding. 7 NP-complete Genes Conditions. 8 ... • bicluster is biclique

Biclustering Algorithms for Biological Data Analysis€¢ If all weights are binary, biclustering becomes biclique finding. 7 NP-complete Genes Conditions. 8 ... • bicluster is biclique

Biclustering via Sparse Singular Value Decompositionjianhua/paper/LeeShenHuangMarron09.pdf · data analysis. Busygin et al. (2008) provide a survey of biclustering in data mining

Biclustering via Sparse Singular Value Decompositionjianhua/paper/LeeShenHuangMarron09.pdf · data analysis. Busygin et al. (2008) provide a survey of biclustering in data mining

Poisson Distribution. Poisson Poisson 1781-1840.

Poisson Distribution. Poisson Poisson 1781-1840.

Plaid models, biclustering, clustering on subsets of ...ligarto.org/rdiaz/Papers/Plaid.et.al.pdf · Plaid models, biclustering, clustering on subsets of attributes, feature selection

Plaid models, biclustering, clustering on subsets of ...ligarto.org/rdiaz/Papers/Plaid.et.al.pdf · Plaid models, biclustering, clustering on subsets of attributes, feature selection