2003 Royal Statistical Society 0039–0526/03/52689

The Statistician (2003)52, Part 4, pp. 689–705

Book reviews

Books for reviewIf you would like to review a book, and thereby to retain it for your collection, please contactthe Book Reviews Editor, whose details can be found by clicking on ‘books currently available’in the information on the Royal Statistical Society’s Web site:

http://www.rss.org.uk/publications/

Reviews in this issueChristakos, G., Bogaert, P. and Serre, M. Temporal GIS: Advanced Functions for Field-based

Applications, p. 690Coggan, D. Statistics in Clinical Practice, p. 690Cohen, J., Cohen, P., West, S. G. and Aiken, L. S. Applied Multiple Regression/Correlation

Analysis for the Behavioral Sciences, p. 691Davis, C. S. Statistical Methods for the Analysis of Repeated Measurements, p. 691Diggle, P. J., Heagerty, P., Liang, K.-Y. and Zeger, S. L. Analysis of Longitudinal Data, p. 692Dobson, A. An Introduction to Generalized Linear Models, p. 694Doucet, A., de Freitas, N. and Gordon, N. (eds) An Introduction to Sequential Monte Carlo

Methods, p. 694Everitt, B. S. Statistics for Psychologists: an Intermediate Course, p. 695Everitt, B. S. and Dunn, G. Applied Multivariate Analysis, p. 696Franses, P. H. and van Dijk, D. Non-linear Time Series Models in Empirical Finance, p. 696Glaz, J., Naus, J. and Wallenstein, S. Scan Statistics, p. 697John, J. A., Whitaker, D. and Johnson, D. G. Statistical Thinking for Managers, p. 698Kotz, S., Kozubowski, T. J. and Podgórski, K. The Laplace Distribution and Generalizations: a

Revisit with Applications to Communications, Economics, Engineering, and Finance, p. 698Lantuejoul, C. Geostatistical Simulation, p. 699Lauritzen, S. L. Thiele: Pioneer in Statistics, p. 700Lindsey, J. K. Nonlinear Models in Medical Statistics, p. 701Pollock, D. S. G. A Handbook of Time-series Analysis, Signal Processing and Dynamics, p. 701Rosenblatt, J. Basic Statistical Methods and Models for the Sciences, p. 702Thode, H. C. Testing for Normality, p. 703Turkington, D. A. Matrix Calculus and Zero–OneMatrices: Statistical and Econometric Appli-

cations, p. 703Venables, B. and Ripley, B. Modern Applied Statistics with S, p. 704Zivot, E. and Wang, J. Modeling Financial Time Series with S-Plus, p. 705

690 Book Reviews

Temporal GIS: Advanced Functions for Field-basedApplicationsG. Christakos, P. Bogaert and M. Serre, 2002Berlin, Springerxii + 218 pp., £42.00ISBN 3-540-41476-2

This resembles a manifesto and really needs to beread in conjunction with Christakos (2000) for besteffect. The authors try to contrast their method-ology, based on Bayesian maximum entropy andwhich requires some understanding of the underly-ing physical models, with ‘statistical’ methods suchas those developed by Diggle et al. (1998). For exam-ple, kriging is presented as a special case of theapproach that they advocate, which is applicableunder certain narrow conditions. This seems a ratherabrupt distinction in practice, especially given theauthors’ assertion later (page 192) in which it isacknowledged that scientists may find someapproaches most suitable for certain parts of aninvestigation. However, this contrast serves to differ-entiate the material that is contained in the book.The philosophical nature of parts of the book isacknowledged in the preface, but the authors statetheir intention of focusing on ‘critical practicality’which they define as using temporal geographicalinformation systems to gain a better scientific under-standing of the phenomena under investigation, toprovide accurate predictions and to make usefuldecisions. Chapter 7 of the book introduces BME-Lib, a set of MATLAB software also included ona compact disc with the book. Again, familiaritywith Christakos (2000) is required to understandthe methodology that is contained within BMELib.

Clearly this is a specialized book. It makes noclaims about an intended audience but seems to im-ply that it is aimed at temporal geographical infor-mation systems specialists and expresses the hopethat they may be able to communicate at a morefundamental scientific level with the various scien-tific specialists with whom they are involved. Partof the approach that is advocated is to maximizethe use of physical models of the phenomena un-der study. The software that is provided requiresMATLAB 5.0 or newer (which obviously rules outapplications such as Octave) and should run underany environment which can run MATLAB. Modifi-able example scripts are included with the softwarewhich make it as easy to use as it could be given aone-chapter written summary.

163 references are cited, 32 of which are to theauthors’ own work. Indexing is modest, printed onfewer than three pages, which may reflect the philo-sophical nature of much of the book. The authorsargue the case for using this type of methodologyrather more than for giving a very clear exposition of

either the theory or detailed case-studies using themethodology. Nevertheless it presents a thought-provoking argument about the nature of the scien-tific method which is of interest in its own right (it isparticularly scathing about simple reliance on Ney-man–Pearson hypothesis testing and the damagethat is done to public perceptions of science whendifferent groups claim contrasting results). How-ever, this is probably not of enough interest in sucha specialized book to merit purchase for this alone.What is achieved is a forcibly presented espousalof both the authors’ overall approach and their re-search that may be of interest to information scien-tists working with spatiotemporal phenomena. Theprovision of MATLAB software provides a realiza-tion of the Bayesian maximum entropy approach totemporal geographical information systems and isvaluable for those who are interested in its applica-tion.

ReferencesChristakos, G. (2000) Modern Spatiotemporal Geosta-tistics. New York: Oxford University Press.

Diggle, P. J., Tawn, J. A. and Moyeed, R. A. (1998)Model-based geostatistics (with discussion). Appl.Statist., 47, 299–350.

Paul HewsonDevon County Council

Exeter

Statistics in Clinical Practice, 2nd ednD. Coggan, 2003London, BMJ Booksviii + 110 pp., £14.95ISBN 0-7279-1609-2

This is a non-mathematical introduction to statis-tics for the health sciences. It does not set out toenable a reader to perform even the most simplestatistical calculation. In not doing so, it is differ-ent from many other statistics text-books that areon the market today. In his preface, David Cogganwrites

‘The bad news for many medical students anddoctors is that these days one cannot practicemedicine without some understanding of statis-tics’.

He follows this up by saying

‘The good news is that clinicians need not bemathematicians to use statistics’.

I am sure that many would agree with his senti-ments.

The book is divided into three broad sections.Chapters 1–3 are concerned with descriptive sta-tistics. Types of data are introduced in Chapter 1,

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followed by graphical presentations for bothunivariate and bivariate data. The graphical pres-entations are thorough. Chapter 4 discusses prob-ability and chance. The first part of this chaptercomments on sensitivity, specificity and predictivevalue in the context of mammography screening.The second part discusses combining probabilities(both the addition rule and the multiplication ruleare defined). He terms the phrase ‘either of twoevents’ for the former and ‘both of two events’ forthe latter, which I like.

Chapters 5–9 deal with statistical inference. Termssuch as hypothesis testing, p-values and confidenceintervals are all introduced and generally well de-scribed. With respect to p-values he says, quite cor-rectly, that the threshold for significance is arbitrary.Another issue lies in the reporting of p-values inthe literature. He asks the question is it better toquote an exact p-value (p = 0:023 say) rather thanan inexact p-value (p < 0:05)? Coggan argues infavour of the former. His supporting statement istaken from car sales. He says that it is more infor-mative to say that a car costs £11500 than to quotethe price under £12000. I argue the opposite, forreasons of simplicity and prefer the binary outcomethat p < 0:05 implies.

Chapter 7 introduces statistical power. Type I andtype II errors are defined in the context of hypothe-sis testing. However, these concepts are not easy tounderstand. I prefer to introduce a type I error as theprobability of a false positive event and a type II er-ror as the probability of a false negative result. As anexample, a type I error is akin to finding an innocentman guilty. The latter part of this chapter discussessampling strategies and the most common (quotasampling, systematic sampling, random samplingand cluster sampling) are outlined.

Chapter 8 comments on statistical modelling. Init, he gives an overview of multiple regression andsurvival data. The last chapter is headed ‘Interpre-tation of statistical analyses’. Though it may bea good heading, I found nothing of use here. Forexample, meta-analysis deserves rather more thanfour short paragraphs. The last section of Chap-ter 9 is entitled ‘Working with statisticians’. Cogganadvocates that the clinician should make contactwith the statistician early. I agree with him here. Healso advocates that the clinical researcher shouldmake some attempt to understand the statisticaltechnique. I agree here as well, but I would go fur-ther and say that the statistician should make someattempt to get to grips with the clinical problem.That way, both clinicians and statisticians wouldbe better served.

The book appears to have been written fromCoggan’s own experiences: this makes easy reading.Perhaps what is missing, though, are references to

support his views. It would have been a better bookfor it. That said, at £14.95 it is not overpriced. Itis ideally suited to the undergraduate medical stu-dent. Post-graduates and those working in medicalresearch are likely to want something of more sub-stance.

Alan S. RigbyUniversity of Sheffield

Applied Multiple Regression/Correlation Analysisfor the Behavioral Sciences, 3rd ednJ. Cohen, P. Cohen, S. G. West and L. S. Aiken,2003Mahwah, Erlbaumxxviii + 704 pp., £41.36ISBN 0-8058-2223-2

This is a heavily revised US text-book for advancedundergraduate and graduate level students andresearchers. The intended audience is unchanged—behavioural and social scientists who want a dataanalytic approach with emphasis on verbal exposi-tion and concrete examples, but not much mathe-matics. The mastermind behind the first edition diedsoon after the planning stage. The other three au-thors have kept the central philosophy of treatingmultiple correlation and regression and the analy-sis of variance as a unified methodology. They have,however, shifted emphasis from significance teststo the use of confidence intervals and effect sizemethods.

The new chapters are Chapter 4, on assumptionsand default remedies, Chapter 10, on problems ofoutliers and multicollinearity, Chapter 13, on logis-tic and Poisson regression, Chapter 14, on multi-level models for clustered data, and Chapter 15, onlongitudinal data. Heavily revised chapters includeChapter 6, on curvilinear relationships and trans-formations, Chapter 7, on interactions between con-tinuous variables, Chapter 9, on interactions withcategorical variables, Chapter 11, on missing data,and Chapter 16, on multiple dependent variablesand set correlation analysis.

This would be a useful book in the library of anapplied statistics department.

Freda KempSt Andrews University

Statistical Methods for the Analysis of RepeatedMeasurementsC. S. Davis, 2002New York, Springerxxiv + 416 pp., £59.50ISBN 0-387-95370-1

Many books have been published on the subject

692 Book Reviews

of the analysis of repeated measurements, and thistext helpfully lists many of them in the introductorychapter. Most of these earlier offerings have goodexplanations of the theory underlying the variousapproaches to this type of analysis, and many con-tain real data. The main attraction of the currenttext is the very practical approach and the wealthof worked examples. In addition to these workedexamples, each chapter ends with a comprehensiveset of ‘homework’ problems, making this an excel-lent choice for both students and teachers. The bookis based on a graduate course that was taughtby the author, and the data that are used in theworked examples are available at http://www.springer-ny.com.

Chapter 1 introduces the problem: data consist-ing of a single response variable measured at mul-tiple times (or under multiple conditions) for a sin-gle subject, with or without covariates related tothe response. In practice, of course, usually the re-peated measurements are taken over time and thetemporal effect is of interest in the analysis, but theauthor emphasizes that this need not be so. Thischapter concludes with brief notes and references onsample size estimation and the problems of missingdata.

Chapters 2 and 3 discuss methods in which simpleunivariate summaries of the multiple measurementsare analysed—summaries such as regression slopes,areas under the curves and correlations. Methodsusing simple multivariate summaries of the data arealso described.

Chapters 4 and 5 discuss more structured normaltheory methods. Multivariate analysis of variance iscovered first, with sections on profile analysis andgrowth curve methods. Repeated measures analy-sis of variance is discussed in Chapter 5, coveringone-sample problems and describing the sphericitycondition and multiple-sample problems.

Linear mixed models are then introduced—thismethodology seems nowadays to be widely used foranalysing repeated measures data, now that soft-ware is available to allow the computations to beperformed with relative ease; this chapter may havemost interest for many practitioners who are new tothis type of analysis.

The final four chapters discuss more recent devel-opments in the analysis of non-normal repeatedmeasures data—categorical outcomes, contingencytables, approaches based on generalized linear mod-els and, finally, nonparametric methods.

The structure of the text is appealing: each chap-ter introduces the principles behind the methodsbeing discussed and contains a brief but adequateintroduction to the underlying theory. Worked exam-ples, using real data, are then provided for each ofthe methods, and the advantages and disadvantages

of each approach are summarized. The exercises atthe end of each chapter give many more examplesusing real data.

As the author suggests, this text is more compre-hensive than most others on the subject of repeatedmeasurements, and it is aimed more clearly at thestudent, the teacher and the applied statistician. Foreach of these audiences it seems eminently suitable,providing a good coverage of essential theory andexcelling in the provision of many examples of realdata and worked analyses. The writing style is goodand the arguments and the development of ideas areeasy to follow. Since data sets with repeated mea-surements are so commonplace, I expect this bookto be popular: it is a well-written and practical guideto the subject and can be thoroughly recommended.

Les HusonGNB Limited

Chislehurst

Analysis of Longitudinal Data, 2nd ednP. J. Diggle, P. Heagerty, K.-Y. Liang andS. L. Zeger, 2002Oxford, Oxford University Press396 pp., £40.00ISBN 0-19-852484-6

This new edition is a thorough and expanded revi-sion of the first edition of the text that was firstpublished in 1994. This second edition includes twonew chapters, the first on fully parametric modelsfor discrete repeated measures data and the secondon statistical methods for time-dependent predic-tors.

This book

‘describes statistical models and methods for theanalysis of longitudinal data, with a strong em-phasis on applications in the biological andhealth sciences’.

The book is split into 14 chapters. The first threechapters provide an introduction to the subject andcover basic issues of design and exploratory anal-ysis. Chapters 4–6 develop linear models and asso-ciated statistical methods for data sets in which theresponse variable is a continuous measurement.Chapters 7–11 are concerned with generalizedlinear models for discrete response variables. Chap-ter 12 discusses time-dependent covariates. Chap-ter 13 considers how to deal with missing valuesin longitudinal studies. Chapter 14 gives a briefaccount of some additional topics including non-parametric modelling of the mean response, non-linear regression modelling and multivariatelongitudinal data. An appendix provides a brief

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review of some basic statistical concepts that areused throughout the book. The book ends with alist of references and an index.

Each of the chapters is well laid out, quicklybringing the reader to the questions of interest andto the main analyses. The concepts and methodsare illustrated throughout the book with extensiveexamples from the biological and health sciencesarea. Under each heading, worked examples arepresented in parallel with the methodological devel-opment.

In the preface the authors state that they havechosen not to discuss statistical software explicitlyin the book. Personally, as a practical statistician Ithink that this is a mistake, as I would have likedmore guidance on what software to use to carry outthe various analyses. In their defence, the authorsstate that none of the currently available packages(S-PLUS, MLn, SAS or Genstat) contains enoughfacilities to cope with the full range of longitudi-nal data analyses that are covered in the book. Theauthors mention that they used the S system foranalysis, with additional user-defined functions forlongitudinal data analysis. Heagerty’s Web site sug-gests that analysis programs may be available but(see below) this is not so. Most packages containprewritten functions for analysing longitudinal datasets in which the response variable is a continuousmeasurement. Fairclough (2002) describes the SAScode for various statistical models for analysing re-peated measures of quality-of-life outcomes, whichshe regards as continuous measurements. Similarly,Everitt (2001) and Rabe-Hesketh and Everitt (2000)provide practical descriptions of the analysis oflongitudinal data with continuous outcomes whenusing S-PLUS and STATA respectively.

The book provides a useful chapter on anal-ysis of variance methods and offers convincingreasons why analysis of variance is not to be recom-mended as an approach for the analysis of longitu-dinal data, i.e. it fails to exploit the potential gainsin efficiency from modelling the covariance amongrepeated observations and it is a simple meth-od that requires a complete balanced array ofdata.

The book then goes on to introduce three broadapproaches to the analysis of discrete and con-tinuous longitudinal data by using extensions ofgeneralized linear models. The three extensions ofgeneralized linear models for longitudinal data thatare discussed are marginal, random-effects andtransition models. The next three chapters (Chap-ters 8–10) present details about each method andextensive examples of their use. For example, mar-ginal models are appropriate when inferences aboutthe population average are the focus. In a clinicaltrial the average difference between the control and

treatment is most important, not the differences forany one individual, so a marginal model is moresuitable.

Chapter 12 contains a useful discussion of theimportant analytical issues that arise with time-varying covariates in observational studies. Miss-ing values are a common problem in the analysis oflongitudinal data. So the next chapter discusses theproblem of missing data and describes methods ofimputing missing values. Missing data are a particu-lar problem in studies with quality-of-life outcomes.Again Fairclough (2002) provides a more compre-hensive discussion of missing values and a specialedition (1998, volume 17) of the journal Statisticsin Medicine was devoted to this topic.

The final chapter on additional topics gives shortintroductions to four topics. These four topicsinclude nonparametric modelling of the mean re-sponse, non-linear regression modelling, joint mod-elling of longitudinal measurements and recurrentevents and multivariate longitudinal data. Althougheach topic has only a short introduction, the topicscan be easily pursued in greater detail through theliterature that is cited. A limitation is that there isno discussion of what software could be used to fitthe models.

The book gives addresses for two of the authors’(Diggle and Heagerty) personal Web sites. Heag-erty’s site is far more useful and helpful; it has aspecific link for the book and includes subsectionson data sets, analysis programs, authors and errata.The data sets and accompanying documentationwere easily downloaded from the Web as text files.Unfortunately at the time of this review the anal-ysis programs were ‘not yet available’. As a prac-tical medical statistician I would have found theprograms extremely helpful, particularly thoseprograms for the analysis of discrete or binarylongitudinal responses, which are rarely discussedin specialist software books. There are also someerrata ranging from mistakes in references to mis-takes in the equations.

This excellently presented book achieves its aimsof describing statistical models and methods forthe analysis of longitudinal data. For most statis-ticians involved in the analysis of longitudinal datait provides a comprehensive reference for the vari-ous methods and models. However, in my opinionit is a little weak on the practical computing as-pects of the analysis of longitudinal data, i.e. whatsoftware and options one should use. For this onemay wish to consult the references already cited,although these texts only provide guidance on themore simple analyses when the response can beregarded as a continuous measurement.

Overall, I would recommend it for library pur-chase for any statistician intending to analyse longi-

694 Book Reviews

tudinal data in research studies regularly. However,those who are involved in the statistical analysis ofsuch data may find that they must consult othertexts and software reference manuals for details ofhow to fit the various models to such data.

ReferencesEveritt, B. S. (2001) A Handbook of Statistical Anal-yses using S-Plus, 2nd edn. London: Chapman andHall–CRC.

Fairclough, D. L. (2002)Design andAnalysis of Qualityof Life Studies in Clinical Trials. New York: Chap-man and Hall.

Rabe-Hesketh, S. and Everitt, B. (2000) A Handbookof Statistical Analyses using Stata, 2nd edn. London:Chapman and Hall–CRC.

Stephen WaltersUniversity of Sheffield

An Introduction to Generalized Linear Models,2nd ednA. Dobson, 2002Boca Raton, Chapman and Hall–CRCx + 226 pp., £24.99ISBN 1-58488-165-8

The author says,

‘Statistical tools for analyzing data are develop-ing rapidly so that the 1990 edition of this book,“An Introduction to Statistical Modelling”, isnow out of date. . . . This new edition has been ex-panded to include nominal (or multinomial) andordinal logistic regression, survival analysis andanalysis of longitudinal and clustered data. . . .The new edition relies on numerical methods[much] more than the previous edition did. . . .There is [now] an emphasis on graphical methodsfor exploratory data analysis, visualizing numer-ical optimization (for example, of the likelihoodfunction) and plotting residuals to check the ade-quacy of models.’

The book does not contain the data sets or out-line solutions of the exercises, but these are availablefrom the publisher’s Web site

www.crcpress.com/us/ElectronicProducts/downandup.asp?mscssid=

The original purpose of clearly presenting a uni-fied framework for statistical modelling to under-graduates and researchers in applied fields has beenmaintained. Many recent advances have been incor-porated.

Freda KempSt Andrews University

An Introduction to SequentialMonte CarloMethodsA. Doucet, N. de Freitas and N. Gordon (eds),2001New York, Springerxxviii + 582 pp., £53.18ISBN 0-387-95146-6

When new observations arrive sequentially in time,one can perform Bayesian inference on line bysequentially updating the posterior distribution. Fora linear Gaussian state space model, the widely usedKalman filter provides an exact analytical expres-sion for the evolving sequence of posterior distri-butions. Similarly, when the data are modelled as apartially observed, finite state space Markov chain,the hidden Markov model filter gives an analyti-cal solution. In general, however, solutions for non-Gaussian data exhibiting high dimensionality andnon-linearity depend on simulation and moderncomputing power.

Over 50 experts drawn from many fields havejoined in producing this comprehensive overview ofthe great progress that has been made in this area inthe last decade. The 26 chapters have been writtenby small groups of people; each chapter has beenreviewed by other contributors and by outside ref-erees. Mostly the chapters are self-contained andcan be read independently of one another by any-one with a reasonable understanding of probabilitytheory. Many algorithms are provided. Within eachpart the chapters are arranged alphabetically byauthors.

Part I contains an introductory chapter by thethree editors. It states their motivation in writingthe book, expounds the basic problem underlyingsequential Markovian, non-linear, non-Gaussian,state space models and discusses perfect MonteCarlo sampling, importance sampling and the boot-strap filter. Part II contains two papers on theoret-ical issues, one taking a theoretical look at particlefilters (Crisan) and one on interacting particle fil-tering with discrete observations (Del Moral andJacod).

The 11 chapters in part III are about strategies forimproving sequential Monte Carlo methods. Theirtitles give some idea of the coverage: 4, ‘SequentialMonte Carlo methods for optimal filtering’ (And-rieu, Doucet and Punskaya); 5, ‘Deterministic andstochastic particle filters in state space models’(Bølviken and Storvik); 6, ‘RESAMPLE-MOVEfiltering with cross-model jumps’ (Berzuini andGilks); 7, ‘Improvement strategies for Monte Carloparticle filters’ (Godsill and Clapp); 8, ‘Approxi-mating and maximising the likelihood for a gen-eral state-space model’ (Hürzeler and Künsch); 9,‘Monte Carlo smoothing and a self-organising state-space model’ (Kitagawa and Sato); 10, ‘Combined

Book Reviews 695

parameter and state estimation in simulation basedfiltering’ (Liu and West); 11, ‘A theoretical frame-work for sequential importance sampling withresampling’ (Liu, Chen and Logvinenko); 12, ‘Im-proving regularised particle filters’ (Musso, Oud-jane and Le Gland); 13, ‘Auxiliary variable basedparticle filters’ (Pitt and Shephard); 14, ‘Improvedparticle filters and smoothing’ (Stavropoulos andTitterington).

In part IV the 12 chapters all involve applications.Chapter 15 is on ‘Posterior Cramér–Rao boundsfor sequential estimation’ (Bergman) with an appli-cation to terrain navigation. This is followed by16, ‘Statistical models of visual shape and motion’(Blake, Isard and MacCormick), 17, ‘SequentialMonte Carlo methods for neural networks’ (de Fre-itas, Andrieu, Højen-Sørensen, Niranjan and Gee),and 18, ‘Sequential estimation of signals undermodel uncertainty’ (Djuric).

In Chapters 19, 20 and 21 the applicationsare ‘Particle filters for mobile robot localization’(Fox, Thrun, Burgard and Dellaert), a time-varying frequency wave in small count data (‘Self-organizing time series model’, by Higuchi) anddynamic Bayesian networks, e.g. in traffic surveil-lance (‘Sampling in factored dynamic systems’, byKoller and Lerner). The next three applications arethe laboratory growth of silicon–germanium on asilicon substrate (22, ‘In-situ ellipsometry solutionsusing sequential Monte Carlo’, by Marrs; 23,‘Manoeuvring target tracking using a multiple-model bootstrap filter (McGinnity and Irwin)) andconcurrent localization and learning for a mobilerobot (24, ‘Rao-Blackwellised particle filtering fordynamic Bayesian networks’, by Murphy andRussell). Chapter 25 studies missile guidance as astochastic control problem (‘Particles and mixturesfor tracking and guidance’, by Salmond andCordon) and finally there is 26, ‘Monte Carlo tech-niques for automated target recognition’ (Sri-vastava, Lanterman, Grenander, Loizeaux andMiller).

The references are consolidated into a 24-pagebibliography and there is a five-page index.

The last decade has seen many papers that are onsequential Monte Carlo methods and algorithms(bootstrap filters, Monte Carlo filters, condensa-tion, particle filters and interacting particle approx-imations). These have been developed and appliedby researchers in many fields. This book successfullyachieves the editors’ wish to introduce such meth-ods to a wider audience. It gives insight into theway that Monte Carlo methods are developing inareas that are as diverse as terrain navigation, targettracking and missile guidance, financial modelling,computer vision, neural networks, time series fore-casting, machine learning, robotics and industrial

process control. The book should be in everyuniversity library.

Freda KempSt Andrews University

Statistics for Psychologists: an Intermediate CourseB. S. Everitt, 2001Mahwah, Erlbaumvi + 378 pp., £49.50ISBN 0-8058-3836-8

Everitt’s book is aimed at the large post-graduatepsychology market. The book builds on the statis-tics courses that are typically taken by undergrad-uate psychology students, covers more advancedtechniques and applies them to real world prob-lems. The techniques include analysis of variance,regression, log-linear modelling, longitudinal meth-ods and computationally intensive methods (i.e.the bootstrap). The style is typical of Everitt: clearwithout patronizing his readers.

Unlike in most general texts Everitt dedicates awhole chapter to graphing techniques, and hestresses the importance of graphing throughout allthe other chapters (see also Tufte (2001) and Wrightand Williams (2003)). Everitt’s approach to statis-tics is as a problem solving pursuit rather than amathematical challenge, and thus it is ideally suitedfor applied statistics. The mathematical and com-putational detail is enough to give the interestedreader knowledge of the techniques, but it is notcentral to the book. This is a plus point for manypsychologists, and other social scientists, who areoften terrified of mathematical rigour.

This book is not supposed to be a guide to astatistics program, yet Everitt does include some‘computer hints’ for each of the procedures. Thesereally are only hints in carrying out the analyses andwould not enable a reader to interpret the output.He does mention some specific oddities about SPSSand S-PLUS that you might not learn from a morebasic text.

Although the content of the book might not betoo widely accessible to undergraduate students, itcould be particularly useful for educators of suchstudents. Everitt goes into more detail on some ofthe procedures than many students would like, butthat students would hope their educators know. Itprovides an excellent guide to some more advancedtechniques in statistics but will be beyond most psy-chology undergraduates.

We believe that Everitt’s book is suited to mostpost-graduate students. The coverage of topics re-flects what is taught in most post-graduate courses.The level of explanation is enough to provide theinterested reader with a good understanding of the

696 Book Reviews

techniques. Where more discussion could be madeon a topic, Everitt provides references for furtherreading.

This book stands apart from many otherintermediate text-books in its use of real data sets.Too often statistics text-books attempt to make thesubject area more interesting by including exam-ple data sets from made-up research. The idea thatstudents would be more interested if the examplesinvolve sex or alcohol is condescending, and argu-ably detrimental to learning. Our students thinkpsychology exciting; that is why they do thecourse!

This book is clearly and interestingly written, animportant aspect of a statistics text for this particu-lar audience. Undergraduate students might be putoff by the amount of equations and tables. Somepost-graduates may find some of the equationsdaunting, but others will enjoy the challenge. Aspeople who teach post-graduates, we really liked it,but we feel that as a core text it might be too difficultfor some students.

ReferencesTufte, E. R. (2001) The Visual Display of QuantitativeInformation, 2nd edn. Cheshire: Graphics.

Wright, D. B. and Williams, S. E. (2003). Producingbad results sections. Psychologist, to be published.

Sian Williams and Daniel WrightUniversity of Sussex

Brighton

Applied Multivariate Analysis, 2nd ednB. S. Everitt and G. Dunn, 2001London, Arnoldx + 342 pp., £24.99ISBN 0-340-74122-8

This paperback book covers exploratory andgraphical techniques, model fitting and hypothe-sis testing. It introduces regression and analysis ofvariance, generalized linear models, log-linear andlogistic models, clustering and classification, prin-cipal components and factor analysis, corres-pondence analysis and multidimensional scaling,models for multivariate response variables, includ-ing repeated measures, covariance structure andstructural equation models and path analysis.

This expanded second edition contains new mate-rial on correspondence analysis, neural networksand extensions of the generalized linear model tomultiple-response variables; the material on graph-ical techniques has been revised and expanded, andthere are new examples in all chapters. The bookis aimed at statistics students and researchers withsome basic statistical background who need to use

multivariate methods. The focus of the book is onthe appropriate choice of method and useful inter-pretation of results, and it contains numerous usefuldata sets, many of which are available on the Web.

Chapter 1 introduces multivariate data and theaims of different types of multivariate analysis.Chapter 2 focuses on simple and up-to-date explor-atory graphical methods (scatterplots, bubble plots,co-plots and bivariate box plots, as well as probabil-ity plots). The remaining Chapters 3–13 each coverone of the other main topics listed above. There isalso a list of relevant software (and details of howto obtain more information) in an appendix, plusguidance on how to deal with missing data.

This is not an overly technical book, and themore technical details are mostly presented in sepa-rate boxes so they can be returned to when needed.Where appropriate, the methods are described forone-dimensional data first before generalizing tomultivariate. Both aspects make the book easy toread.

This is an excellent, nicely presented and veryreadable book, at a reasonable price. As well as itsinteresting and well-interpreted examples, there areexercises in each chapter, with selected solutions.Some of the exercises would be suitable for use asstudent projects, making the book useful as a coursetext-book. It has a good index and useful up-to-date references, and it contains a few good quota-tions. I highly recommend this book as an excellentresource for lecturers as well as students and appliedresearchers.

Alison GrayUniversity of Strathclyde

Glasgow

Non-linear Time SeriesModels in Empirical FinanceP. H. Franses and D. van Dijk, 2000Cambridge, Cambridge University Pressxvi + 280 pp., £22.95ISBN 0-521-77965-0

This is a book describing model building and fore-casting for financial asset returns. The level is adver-tised as ‘advanced undergraduate’ and it coversplenty of ground in six chapters and 250 pages. Thecoverage of non-linear models is considerable, es-pecially of regime switching models.

Chapter 1 has a nice discussion of real series,level and returns of currency, but some topics areintroduced rather abruptly, e.g. kernel smoothing.The next chapter is mainly concerned with model-ling linear series and covers the usual linear modelsand, in addition, fractional differencing, forecast-ing, Dickey–Fuller tests, robust methods and more.For 40-odd pages the coverage is good but readers

Book Reviews 697

who are unfamiliar with this material may find thatthey will need extra reading here. The main thrust ofthe chapter is discussion of the models themselvesrather than their identification and estimation.

Chapter 3 takes us to non-linear models and amain topic of this text, non-linear models for re-turns. In time series terms this is a discussion ofsmooth transition autoregressive, self-exciting thres-hold autoregressive and Markov switching models.The coverage is good, albeit a little terse in parts, e.g.on multivariate regime switching. Overall I thinkthat this chapter would prove valuable to the readerwho is meeting these topics for the first time. Thisis a complex area and I think that the authors havedone an excellent job in charting a feasible path forthe reader.

The following chapter takes the regime switch-ing ideas of Chapter 3 and extends them to vola-tility. As we might expect this means discussion ofgeneralized autoregressive conditional heterosced-astic models and their extensions, which are many.Again the coverage is comprehensive. I think thatthe reader who is new to this area may find the pos-sibilities rather confusing. However, to be fair to theauthors this is complex stuff which is handled verywell, indeed as well as or better than I have seenelsewhere.

The last chapter discusses neural nets. The levelof coverage is less comprehensive than that of earlierchapters; indeed this is pointed out by the authors.I found this chapter rather less convincing than theprevious chapters but I confess a prejudice on mypart.

I warmed to this book as I read it. It does a veryrespectable job of bringing non-linear time seriesmethods to readers who are interested in finance.It does make demands on the reader but I feel thatthe reader will think that they are very worthwhile.Given the relatively reasonable softback price thisis worthwhile to buy.

It would be tough going for my final year stu-dents, especially as there are no exercises. However,in its field there are few competitors and I wouldhappily use it for a Masters programme.

G. JanacekUniversity of East Anglia

Norwich

Scan StatisticsJ. Glaz, J. Naus and S. Wallenstein, 2001New York, Springerxvi + 370 pp., £63.00ISBN 0-387-98819-X

What do the following situations have in common:disease surveillance in time or space; mine-field

detection via remote sensing; galaxy supercluster-ing; deoxyribonucleic acid matching;TheBibleCodeby M. Drosnin? They all produce data that can beanalysed by using scan statistics.

Most monographs begin with theory and endwith applications. Glaz, Naus and Wallenstein havedone the reverse. The first 100 pages of their bookare devoted to applications in fields as diverse as epi-demiology, telecommunications, astronomy, qual-ity control, molecular biology, visual perception andmeteorology. They introduce the various kinds ofscan statistics, together with useful formulae forexact and approximate probabilities and momentsof scan statistics, and details of available computerprograms and tables.

Part I, ‘Methods and applications’, contains sixchapters. The first, ‘Introduction’, defines a discretescan statistic, uses Daniel Bernoulli’s 1734 study ofthe inclinations of the planes of the orbits of theplanets (six were then known) to introduce scanstatistics in two dimensions, considers the powerof a scan statistic and ends with a caution aboutclusters and intuition, mentioning birthday coinci-dences and Drosnin’s The Bible Code.

Chapters 2 and 3 deal with ‘Retrospective scan-ning of events over time’ and ‘Prospective scanningof events over time’ respectively. The first looks ata uniform distribution of events over time; the sec-ond at a Poisson process. The fundamental types ofproperties of scan statistics are considered.

Chapter 4 considers the binomial distribution ofevents in discrete time, with and without condi-tionality. The applications here include the lengthof the longest success run, two-zone control chartsfor the mean, acceptance sampling, hot streaks insports, learning studies, reliability theory, a general-ized birthday problem and clusters of wins in chess.Higher dimensional scans are the topic in Chapter 5.This includes scanning in two dimensions and theeffect of the shape of the scanning window. Pros-pecting for uranium deposits, geographical clustersof cancer cases, scanning for the source of muonsand estimating population size in space or time aresome of the examples. The first part of the book endswith a discussion in Chapter 6 of the very importantuse of scan statistics in deoxyribonucleic acid andprotein sequence analysis. All this material is clearlypresented in a fascinating and easy-to-read way.

Part II of the book, ‘Scan distribution theoryand its developments’, is necessarily more math-ematically demanding as it is intended for gradu-ate level probabilists and mathematical statisticianswho wish to study the subject in detail and to carryout further research. One- and two-dimensional dis-crete and continuous scan statistics are covered inconsiderable detail; both exact and approximate re-sults are derived. An idea of the completeness of

698 Book Reviews

the coverage is given by the chapter titles: 7, ‘Ap-proaches used for derivations and approximations’;8, ‘Scanning N uniform distributed points: exact re-sults’; 9, ‘Scanning N uniform distributed points:bounds’; 10, ‘Approximations for the conditionalcase’; 11, ‘Scanning points in a Poisson process’;12, ‘The generalized birthday problem’; 13, ‘Scanstatistics for a sequence of discrete I.I.D. variates’;14, ‘Power’; 15, ‘Testing for clustering’; 16, ‘Two-dimensional scan statistics’; 17, ‘Number of clus-ters: ordered spacings’; 18, ‘Extensions of the scanstatistic’. There is an extensive bibliography con-taining approximately 600 references and an index.A short list of errata has been inserted into the bookand the authors say that other errors will be postedat http://merlot.stat.uconn.edu/∼joe/.

The authors have written two books in one. Part Icarries the reader along so easily and is only 100pages long—there is now no excuse for those whodescribe themselves as statisticians to be ignorantabout this fast developing area of statistics. Thispart may get you ‘hooked’ onto the subject and youmay wish to continue by reading parts of part II.Part II is targeted at a different readership. Its aimis to consolidate the subject and to encourage itsfurther development. If you are looking for an areain which to do research, then this book provides a‘hot’ opportunity that should not be missed.

Freda KempSt Andrews University

Statistical Thinking for ManagersJ. A. John, D. Whitaker and D. G. Johnson, 2001Boca Raton, Chapman and Hall–CRCxii + 282 pp., £24.99ISBN 1-58488-248-4

This is a workbook for managers in the manufac-turing, processing and service industries. Its aim isto enable the user to think statistically by acquiringbasic statistical ideas with the minimum of expo-sure to mathematics and computation. Instead itleans heavily on the use of the spreadsheet Micro-soft EXCEL for plotting and analysing data (a13-page appendix gives an introduction to the pack-age and further information is available from thebook’s Web site, http://www.crcpress.com).A standard deviation, for instance, is treated pri-marily as a tool. The authors’ philosophy is to re-gard an appreciation of its use in the diagnosis andunderstanding of statistical problems as more cru-cial than involvement in its calculation.

The book is set out clearly, with spaces for graphs,tables and written answers to the many problemsthat are posed. Users are strongly urged to use thesespaces for their answers and thus to keep a record

of all their work. There does not appear to be aninstructor’s manual with suggested answers. This isa pity because many of the questions are suitable forclassroom discussion and suggested answers wouldhelp instructors to structure such discussion. Anindication of the approximate amount of time thatis to be spent on each question would also be help-ful. In the early chapters some answers will rely onhunch feeling; the same data may then reappear ina later chapter after the reader has been led into amore statistical approach.

Several practical experiments, which at first sightmay appear simplistic, are key features of the book,e.g. the beads experiment in which a paddle withcavities enables fixed sized samples of red and whitebeads to be obtained. When it is not possible to con-duct these in a classroom situation, a spreadsheetversion of the experiment may be a possibility.

The essential ideas that the book tries to impartare as follows: define clearly what is being studied;have good operational definitions; decide how tocollect the data that you need; collect interval datawhenever possible; think carefully how to presentthe data; plot your data; keep it simple; take advicewhen necessary. A grasp of these principles wouldbe valuable for any manager. What the book doesnot do is give the mathematical rationale underlyinga hypothesis test such as a χ2-test of goodness of fitor the square of the multiple-correlation coefficientfor interpreting a regression analysis. The analysisof variance is well beyond its scope.

This book will do good, so far as it goes, espe-cially if it is used in a classroom situation with anexperienced instructor. I would not recommend iton its own for self-tuition.

Freda KempSt Andrews University

The Laplace Distribution and Generalizations:a Revisit with Applications to Communications,Economics, Engineering, and FinanceS. Kotz, T. J. Kozubowski and K. Podgórski, 2001Boston, Birkhäuserxviii + 350 pp., £72.00ISBN 0-8176-4166-1

The authors’ aim has been to give

‘a systematic exposition of all that appeared inthe literature and was known to us by the endof the 20th century about the Laplace distribu-tion and its numerous generalizations and exten-sions’.

As the authors themselves say, ‘much of the bookis a synthesis of other people’s work’. The contribu-tions of researchers such as Azzalini, Balakrishnan

Book Reviews 699

and Barndorff-Nielsen, as well as those of Kotz,Kozubowski and Podgórski, are incorporated.

It is written, not as a research monograph, butin the form of a text-book for senior undergradu-ates and graduates. The suggestion is made that itwould be suitable for seminars or for a short half-term course in statistics, applied probability andfinance. This could be feasible in the USA but not,I think, in a UK academic context.

The book is in three parts: I, ‘Univariate distri-butions’; II, ‘Multivariate distributions’; III, ‘Appli-cations’. Prerequisites are calculus, matrix algebraand previous courses on basic probability theoryand statistical inference. The pace is gentler in part Ithan in part II. Skipping is not advisable, however,as each chapter in the first two parts assumes thatall previous material has been mastered. There aremany examples, diagrams, tables and exercises (noanswers). Many of the exercises form an integralpart of the exposition; others are for further interest.

The book begins with a historical overview fromLaplace to Keynes. Chapter 2 starts the seriousexposition with material on the classical symmetricLaplace distribution. This chapter is reminiscent ofchapter 24, ‘Laplace (double exponential) distribu-tion’, in Johnson et al. (1995), both in content andordering. It is necessarily much longer; it ends withover 60 exercises.

Chapter 3 gives a systematic coverage of the asym-metric Laplace distribution. Distributions relatedto the Laplace distribution, in particular the Bessellfunction distribution and the Linnik distribution,are studied in Chapter 4.

Part II begins with the short Chapter 5 on thesymmetric multivariate Laplace distribution, fol-lowed by the lengthy Chapter 6 on the asymmetricmultivariate Laplace distribution. The material inthese two chapters does not seem to have appearedin book form previously. I had hoped that the prom-ised Continuous Multivariate Distributions, volume2, by Johnson, Kotz and Balakrishnan would haveappeared by now and would have contained cover-age of this material.

For me the potentially most interesting part ofthe book is part III, ‘Applications’. Here there arefour chapters, on ‘Engineering science’ (11 pages),‘Financial data’ (14 pages), ‘Inventory managementand quality control’ (six pages) and ‘Astronomy andthe biological and environmental sciences’ (fivepages). The authors feel strongly that the normal(Gaussian) distribution has

‘reigned almost without opposition in statisticaltheory and applications for almost two centu-ries’.

In these four final chapters their aim is to showthat there are many situations where Laplace-type

distributions provide the wiser choice of model.This is a well-organized book, with tables of

abbreviations and the most commonly used ter-minology. There is a short appendix on Bessellfunctions and a seven-page index. A very exten-sive literature search has been carried out—the bib-liography contains over 400 references.

The authors’ hope is that their book

‘will trigger additional theoretical research andprovide tools that will generate further fruitfulapplications of the[se] distributions’.

If you are interested in researching into propertiesand applications of alternatives to the normal dis-tribution, then this is the book for you.

ReferenceJohnson, N. L., Kotz, S. and Balakrishnan, N. (1995)Continuous Univariate Distributions, vol. 2, 2nd edn.New York: Wiley.

Freda KempSt Andrews University

Geostatistical SimulationC. Lantuejoul, 2002Berlin, Springerxiv + 256 pp., £37.00ISBN 3-540-42202-1

Christian Lantuejoul’s book Geostatistical Simu-lation is a book about much more than simu-lation methods. It discusses both theory andalgorithms for a range of models and covers muchmore than the Gaussian random fields that weoften see coined with the word ‘geostatistical’. Theauthor approaches the topic in a very systematicway, dividing the book into three parts. The firstpart defines the necessary tools, then the algorithmsare discussed and in the third part several geostatis-tical models are reviewed and the tools and algo-rithms are used to derive simulation algorithms.The tools that are introduced in part I include defi-nitions of random functions, point processes andrandom sets. Variograms and integral ranges arediscussed, basic morphological operations are de-fined and a presentation of some stereological for-mulae based on Minkowski functionals is given.Part II of the book concerns simulation algorithms.It starts by a brief discussion of basic simulationmethods and continues with Markov chain MonteCarlo methods. Tools for calculating theoretical andestimating empirical convergence rates of Markovchain Monte Carlo algorithms are discussed nextand part II is concluded by a brief account of theprinciples behind exact simulation. Part III, whichmakes up half of the book, is devoted to uncon-ditional and conditional simulation of seven com-

700 Book Reviews

mon geostatistical models. The models consideredare Poisson and Cox point processes, Voronoi andPoisson tessellations, Boolean models, object-basedmodels, Gaussian random functions, Gaussian vari-ations and substitution random functions. Eachmodel is presented in a separate chapter in whichLantuejoul first defines the model and then pro-poses simulation algorithms.

The French school of probability is known forits rigorous mathematics and the scene of this bookis also set with a definition of an abstract probabil-ity space, discussion of σ-algebras and probabilitymeasures on families of random closed sets. Nev-ertheless the book is very practical and apart froma few theoretical remarks it can be read by anyonewith basic knowledge of probability. The technicalmathematical comments can be safely ignored andLantuejoul successfully explains the ideas and intu-ition behind important concepts.

Many topics are treated in this book and con-sequently the author has been forced to select hismaterial carefully. The information in the book isvery dense and there is only just enough informa-tion for a reader to implement and understand thesimulation methods that are discussed. I like thisapproach; the book is like a handbook and all majormodels are treated. For readers who have a specialinterest in certain topics it will not be difficult tofind further sources of information from the com-prehensive list of references. This book should bepart of the toolbox of anyone who is interested insimulation from geostatistical models.

Anders BrixRisk Management Solutions

London

Thiele: Pioneer in StatisticsS. L. Lauritzen, 2002Oxford, Oxford University Pressviii + 264 pp., £65.00ISBN 0-19-850972-3

I remember my delight and surprise when, as a post-graduate student studying discrete distributions, Ifirst met T. N. Thiele. It was in the pages of volume 2of the Annals of Mathematical Statistics (1930), 20years after his death. Here was the Poisson distri-bution derived via its half-invariants (cumulants).Why had I not seen his work in my text-books?

Thiele wrote his masterwork, Almindelig Iagtta-gelseslære. . . , in 1889. His less advanced book,Elementær Iagttagelseslære, was written in 1897,translated into English under the title Theory ofObservations in 1903 and reprinted in the Annalsof Mathematical Statistics in 1931. The core ofThiele: Pioneer in Statistics (Chapter 4) is a transla-

tion by Lauritzen of the advanced 1889 work. HereThiele had not only developed the theory of cum-ulants (half-invariants) but had also initiated theconcepts of likelihood, analysis of residuals and thecanonical form of the linear normal model.

Thiele: Pioneer in Statistics begins with a prefaceand a short introduction with biographical details.Next, in Chapter 2, is Lauritzen’s translation (writ-ten for this book) of Thiele’s 1880 seminal paperon least squares. Here his development of theKalman filter was so ahead of its time that it waslittle understood and became forgotten. Lauritzen’s1981 paper, reprinted from the International Statis-tical Review (ISR), forms Chapter 3; this discussesThiele’s work on time series.

Lauritzen’s translation of Thiele’s 1889 master-piece, under the title ‘The general theory of obser-vations: calculus of probability and the method ofleast squares’, comes next, followed by Hald’s 1981ISR detailed description of Thiele’s contributionsto statistics in Chapter 5. In 2000, also in the ISR,Hald gave a translation of another ground breakingpaper by Thiele, ‘On the halfinvariants in the theoryof observations’. This is reprinted in Chapter 6 andis followed in Chapter 7 by Hald’s ISR review of theearly history of cumulants (half-invariants) and theGram–Charlier series.

Lauritzen’s short epilogue goes some way towardsanswering my question in the first paragraph. LikeR. A. Fisher, Thiele had very poor eyesight whichled him to concentrate on his own work rather thanthat of other people. But, unlike Fisher, he was notsupported by an extensive academic environmentwhere there were others who were willing to masterhis obscure ideas. Also we should not forget thatuntil the mid-1900s the cross-referencing of otherpeople’s work was generally poor.

Lauritzen tells us that the two 1981 ISR paperswere sparked off by Hald’s study of Thiele’s originalpapers in preparation for the 500th anniversary ofthe foundation of the University of Copenhagen.Thiele is now firmly established as a pioneer of sta-tistics, as evinced by his inclusion in Johnson andKotz’s Leading Personalities in Statistical Sciences(1997), Heyde and Seneta’s Statisticians of the Cen-turies (2001) and David and Edwards’s AnnotatedReadings in the History of Statistics (2001).

This (2002) book brings together much materialand will greatly interest all who are interested inthe way that a scientific discipline develops. It epit-omizes Stigler’s law of eponymy (scientific laws arenot usually called after their discoverers) and re-minds us that, whereas some reputations flourishmore than is warranted, others are overlooked.

Freda KempSt Andrews University

Book Reviews 701

Nonlinear Models in Medical StatisticsJ. K. Lindsey, 2001New York, Oxford University Pressxii + 280 pp., £35.00ISBN 0-19-850812-3

This hardback book is composed of 10 chaptersand three appendices, with its aim being to providean introduction to non-linear statistical models inmedicine. This book is meant to be a practical textof mechanistic models that try to explain responses,for models which have a non-normal distributionand/or a non-linear regression function. The bookis at a level that requires a background in advancedstatistics or mathematics. Thus it is geared to thestatistician, and it uses examples analysed by thesoftware R (a spin-off of S-PLUS). Libraries of Rfunctions are free for downloading from Dr Lind-sey’s Web site. The end of each chapter has severalexercises, and a reference list for further reading. Ifound only one typographical error in the book; anomission of the word ‘is’ on page 86.

The first three chapters are titled 1, ‘Basic con-cepts’, 2, ‘Practical aspects’, and 3, ‘Families of non-linear regression functions’. Together they providean overview of non-linear modelling. The first chap-ter discusses statistical modelling (data-generatingmechanisms, probability models, distributions andregression functions), limitations of generalized lin-ear models (definition, choice of distribution, regres-sion functions, dependent observations and infer-ence), definitions of non-linearity (distribution andregression function, models with a linear part, trans-formable linearity and intrinsic and parametereffects non-linearity) and developing regressionfunctions (descriptive versus mechanistic modelsand solving differential equations). The secondchapter covers data (response variables and covari-ates), specifying models (software and modelcomponents), calculating the likelihood (non-linearoptimization, parameterizations, initial estimatesand checking convergence) and inference (modelselection, likelihood regions and goodness of fit).Chapter Three covers

(a) growth curves, the largest section of thechapter, including polynomials, exponentialforms and sigmoidal curves,

(b) sums of exponentials, including compart-ment models, and diffusion models, and

(c) other non-linear functions including powerfunctions, cyclic functions and changepointmodels.

The last seven chapters provide applications inspecific areas of medical research. Chapter Four,‘Epidemiology’, addresses natural populations,growth curves, death-rates, changepoints, recurrent

epidemics and outbreaks of infectious diseases.Chapter Five, ‘Clinical trials’, covers evaluating anew medication and decline of response in a sta-ble state. ‘Quality of life’ (Chapter Six) discusses ef-fects of medical treatment, recurrent events, changeof state, transition probabilities and the analysisof diary cards. ‘Pharmacokinetics’ (Chapter Seven)examines concentrations of drugs in the body, par-ent drug and metabolite, and repeated dosing.Whereas Chapter Eight, ‘Pharmacodynamics’, cov-ers the effects of drugs on the body, continuousresponse and count response, ‘Assays and formu-lations’ is the title of Chapter Nine, and it coversthe following topics: assays, colorimetric enzymeassay, deoxyribonuclease assay, Ames Salmonellamicrosome assay and determining formulations. Thefinal chapter, ‘Molecular genetics’, covers sequenceanalysis, finding genes and their exons, and detect-ing locations of mutations.

Appendix A, ‘Data and model examples from R’,is divided into data, models and likelihoods. Appen-dix B, ‘Stochastic dependence structures’, is dividedinto random effects, time dependence, multivari-ate distributions with correlation matrices, dynamicmodels, Markov processes and duration data.Appendix C provides 23 data tables for the exer-cises at the end of each chapter.

What I like best about this book is the discus-sion of a wide range of applications of non-linearmodels, many of which are provided in examples.This book is not intended for those with clinicalexpertise, unless they have a broad background inadvanced statistics. Thus, this book is of most useto statisticians who are involved in research in med-icine, health care or the pharmaceutical industry.The bibliography is extensive, with 151 references,and the 14-page subject index is also quite useful. Irecommend this book for library purchase.

Mark A. BestEastview

A Handbook of Time-series Analysis, SignalProcessing and DynamicsD. S. G. Pollock, 1999London, Academic Pressxx + 734 pp., £58.95ISBN 0-12-560990-6

This book is intended to be a reference for practi-tioners and researchers in time series analysis andsignal processing, two subjects which the author re-gards as being fundamentally the same. Many textsare available which introduce time series analysis,and any new book must offer something differentto be of interest. There are two aspects of this bookwhich distinguish it from other time series texts:

702 Book Reviews

the depth of the mathematical background and thedevelopment of Pascal and C code implementingthe methods described. Also noteworthy is the help-ful compact disc which comes with the book.

Over half the book is taken up by detailed dis-cussion of the mathematical background to timeseries analysis. This begins with polynomial alge-bra, complex analysis and difference equations be-fore discussing least squares estimation and Fouriertransforms at length. The theory is well presentedwith sufficient examples. Algorithms to carry outthe methods are developed and implemented in Pas-cal (with equivalent C routines also being presentedon the disc); many of these will be useful in areasother than time series analysis.

Once the mathematical background has been cov-ered in detail, the book moves on to the theory oftime series models. Linear filters and autoregressiveand moving average models are described, followedby the spectral representation of stationary stochas-tic processes. Extracting an underlying signal fromobserved data and the prediction of future valuesare also discussed.

Finally, the book examines algorithms for theanalysis of real time series data. The estimationof simple quantities such as the mean and sam-ple autocovariance is followed by least squares andmaximum likelihood estimation of autoregressivemoving average model parameters and nonpara-metric estimation of the spectral density function.An appendix on the compact disc includes two fur-ther chapters on statistical distributions and thetheory of maximum likelihood estimation.

The accompanying disc includes the full text ofthe book in portable document format, as well as thePascal and C code which is developed throughoutthe text. Precompiled examples of the use of manyroutines are included and provide helpful examplesto supplement those in the text. The bibliography isalso included in full and is broken down by subjectand chapter.

The most obvious feature which distinguishes thisbook from comparable texts is the extensive cover-age of the mathematical background to the methodsof time series analysis that is presented. This makesthe book very self-contained. Extensive cross-refer-encing means that it is not necessary to absorb allthe background before moving to the later chapters.The drawback is that the book is more technicallydemanding than many other introductions to timeseries.

Combined with the code included, this book willbe a very helpful reference to anyone wishing todevelop their own code to analyse time series data.The main potential drawback in this respect is thatthe discussion is limited to the analysis of stationarytime series, and some currently popular topics of

research such as Bayesian methods, neural networksand wavelets are not described. However, thosetopics which are covered are clearly and com-prehensively explained. The exposition would beaccessible to a good final year mathematics under-graduate, although the level of specialization meansthat the book is more likely to be useful to post-graduate students or researchers. Practitioners whoare simply interested in analysing time series datawill probably find the book less useful; the mathe-matical background will be unnecessary and thereare few examples involving real data sets.

Stuart BarberUniversity of Leeds

Basic Statistical Methods and Modelsfor the SciencesJ. Rosenblatt, 2002Boca Raton, Chapman and Hall–CRCx + 282 pp., £29.99ISBN 1-58488-147-X

This book is designed for students who are on afirst course in statistics at the graduate or at the fi-nal year undergraduate stage. The text attempts toaddress the needs of those who use statistics butare not statisticians. The author assumes that thereader has a good grasp of mathematics, althoughthe use of calculus is not required. MINITAB isused extensively throughout the text. The MINI-TAB exercises could be done by using other stat-istical packages but, obviously, the format andsyntax that are used for doing these would be differ-ent. The author extensively employs the use ofMonte Carlo methods to illustrate concepts. Thefocus of the text is on the use of applications andmodels to describe and explain techniques. Theauthor has written this text to be accessible to read-ers in a wide range of disciplines. In particular heillustrates the use of statistics in diverse areas ofscience, engineering and medicine. The macros thatare used in the examples and exercises are availablefor downloading at

www.crcpress.com/e products/downloads/download.asp?cat no=C147X

A solutions manual is also available with qualifyingcourse adoptions.

The book is divided into seven chapters: 1, ‘Intro-duction’; 2, ‘Classes of models and statistical infer-ence’; 3, ‘Sampling and descriptive statistics’; 4,‘Survey of basic probability’; 5, ‘Introduction tostatistical estimation’; 6, ‘Testing hypotheses’; 7,‘Basic regression and analysis of variance’. Eachchapter starts with a brief description of the con-tents. The text is then littered with examples, many

Book Reviews 703

using MINITAB, to demonstrate the topic underconsideration. Review exercises are at the end ofeach chapter. Selected answers to problems are atthe end of the book. An epilogue, bibliography andan index are also included.

The text concentrates on trying to equip its read-ers with a body of practical knowledge of the statis-tical tools and techniques that are available for use.A clear understanding of the underlying assump-tions and limitations is presented without the readerbecoming bogged down with formal proofs and sta-tistical formulae. The book has many interestingpractical examples from a wide variety of disciplines.It would have been useful to have some of these fol-lowed through by using the relevant statistical ormathematical techniques. A clearer understandingof the whole procedure rather than part of the pro-cess would help the reader. Too many snippets donot fully explain the concepts and procedures thatare presented. The bibliography is very limited anda more extensive one would have aided the reader infurther research and to help him or her to find exam-ples in this area, particularly when the text does notcover the techniques in sufficient detail. The epi-logue consists of one page only.

The author advocates that this book is of anintroductory nature and that no attempt has beenmade at completeness. To this extent the text meetshis brief. I particularly liked the way that the authorguides the user through the examples, with clearinstructions and diagrams included in the text. Thiscan be employed to check that the user has used theMINITAB package correctly. Obviously as recentupdates of the MINITAB software are producedthis text will need to be updated. The author under-stands the pitfalls of using statistical techniques andcarefully uses MINITAB, with appropriate exam-ples, to demonstrate these. The user, after workingthrough the book, should then have enough back-ground knowledge to be able to start to use rele-vant techniques to solve problems. Further texts orbooks on the techniques would be required to en-able the user to understand and use the statisticalprocedures fully. However, this is a good referencetext for science students.

Susan StarkingsLondon South Bank University

London

Testing for NormalityH. C. Thode, 2002New York, Dekkerx + 480 pp., £103.51ISBN 0-8247-9613-6

This is a comprehensive and fascinating book on

approaches to testing for normality. Given the hugevolume of literature on this subject, writing sucha book must have been a challenge and I foundreviewing it a little daunting. In 460 pages this bookcovers a large amount of ground.

Following an introduction, Chapter 2 covers plotsand regression tests. There is a good discussion ofplotting and of the Shapiro–Wilk test and correla-tion tests. Chapter 3 discusses tests using moments(β1 and β2) and Chapter 4, ‘Other tests for normal-ity’, looks at likelihood ratio tests and other miscel-laneous tests, e.g. tests based on Us.

Chapter 5 is headed ‘Goodness of fit tests’ and isconcerned with tests which are based on the empir-ical distribution function such as the Kolmogorov–Smirnov test. χ2-tests are also included in thischapter. Chapter 6 deals with tests for outliers,Chapter 7 deals with power comparisons and Chap-ter 8 considers censored data. I thought that the dis-cussion of power in Chapter 7 was very informativeand an excellent summary of what is known.

These are followed by two chapters on testingfor multivariate normality and testing for outliers. Ifound both chapters very interesting, and I confessthat much of the material was new to me.

The final three chapters deal with mixtures, give asummary of robust methods and discuss some com-putational issues. There are 100 pages of tables.

I liked this book; it was well written and wellorganized and has comprehensive references to theliterature. The style is clearly for practitioners, withmethods presented, and most of the theory omit-ted but well referenced. There are many usefultables.

The quality of production was excellent and Icould find few errors. I read this book with interest,I learned something and I have changed my views onsome topics. I would have bought this with my ownmoney if I had not had the reviewer’s copy. Thisis an essential library purchase and is well worthconsidering for your own bookshelf.

G. JanacekUniversity of East Anglia

Norwich

Matrix Calculus and Zero–One Matrices:Statistical and Econometric ApplicationsD. A. Turkington, 2002Cambridge, Cambridge University Pressxii + 206 pp., £45.00ISBN 0-521-80788-3

This book develops a mathematical framework thatsimplifies the application of classical statistical pro-cedures to econometric models. One of the major

704 Book Reviews

problems that can arise in such applications is toobtain derivatives of certain complicated functions,e.g. the log-likelihood function. The author writes(page 6)

‘. . . what often happens when one attempts to dothe differentiation by using ordinary calculus isthat one is confronted with a hopeless mess. Itis precisely this problem that has motivated thewriting of this book.’

In an introductory chapter, the statistical proce-dures that are to be used later, maximum likelihoodestimators and some test procedures, are briefly re-viewed. Chapter 2 is concerned with the Kroneckerproduct of matrices and some variants of the vecoperator. Chapter 3 deals with 0–1 matrices, i.e.matrices all of whose entries are 0 or 1. A typicalresult of this chapter is that a certain matrix oper-ation, e.g. the interchanging of two columns, canbe neatly formulated as a multiplication with a suit-ably chosen 0–1 matrix. Obviously, this can greatlysimplify actual calculations. A surprisingly rich col-lection of results of this type is given, and some new0–1 matrices are introduced. The next chapter treatsderivatives of matrices and vectors, the emphasisbeing on computational aspects. The chapter con-tains in particular some very useful tables with over50 differentiation rules.

In the second part (Chapters 5–7) it is shownhow the concepts that are developed in the firstpart facilitate the application of statistical proce-dures to econometric models. For a variety of mod-els, the basic ingredients of the procedures arederived: the score vector, the information matrixand the Cramér–Rao lower bound. Chapter 5 dealswith linear regression models. It begins with thebasic model and then covers regression models withautoregressive disturbances and with movingaverage disturbances. Chapter 6 is devoted to somevariations of the seemingly unrelated regressionequations model. The final chapter concerns itselfwith models that are based on the linear simul-taneous equations model, which is of particularinterest to econometricians. Altogether nine statis-tical models of increasing complexity are discus-sed.

The book requires only a rudimentary knowledgeof matrix algebra and ordinary calculus. However,some familiarity with the relevant statistical con-cepts seems indispensable. In my opinion the bookshould prove useful mainly to practising econome-tricians and statisticians. To appreciate the degreeof the simplification that is achieved by using thetools that are provided in the book, it is sufficientto attempt to do one of the more complicated ma-trix computations in the second half without thesetools.

I fear, however, that some potential readers whoare attracted to the book by the keywords ‘matrixcalculus’ might be disappointed at its coverage. Thescope of the material on matrix calculus is some-what narrow. Many of the important concepts thatwe might expect such as eigenvalues and quadraticforms are only briefly touched on or not mentionedat all.

Lorens A. ImhofAachen University

Modern Applied Statistics with S, 4th ednB. Venables and B. Ripley, 2002New York, Springerxii + 496 pp., £52.50ISBN 0-387-95457-0

The first edition of this book was published in 1994;since then the S environment has expanded greatlyand various implementations are now available. Thethird edition of the book was given a detailed reviewby Fernández (2002). That edition catered forS-PLUS versions 3.3 upwards, especially versions5.x, 4.5 and 2000.

This new fourth edition concentrates on versions6 and later of S-PLUS and (please note) versions1.5.0 and later of R (R is the open source imple-mentation of S-PLUS). The examples in this newedition have been computed by using S-PLUS onSolaris and have been tested by using S-PLUS forWindows version 6.0, release 2, and S-PLUS 6.0on LINUX. The implementation-specific details aregiven in Appendix A, the S-PLUS graphical userinterface is discussed in Appendix B and on-linesites for the authors’ software enhancements andthe data sets for this edition are given in Appen-dix C. Whereas S-PLUS is a commercial product(seehttp://www.insightful.com), R is freelyavailable from http://www.r-project.organd mirrors.

The overall structure of the book is roughly thesame. The four introductory chapters have been re-vised considerably—they are now 1, ‘Introduction’,2, ‘Data manipulation’, 3, ‘The S language’, and 4,‘Graphics’. In addition there are many changes inlater chapters; the order of the chapters is differ-ent and there is a large amount of new material on,for example, overdispersion in binomial and Pois-son generalized linear models, projection pursuitregression, neural networks, exploratory multivari-ate analysis and optimization.

This is a major update of a well-tried and verysuccessful book. At least one copy should be avail-able in the library of every applied statistics depart-ment.

Book Reviews 705

ReferenceFernández, C. (2002) Review of Modern Applied Sta-tistics with S-PLUS (by W. N. Venables and B. D.Ripley). Statistician, 51, 302–303.

Freda KempSt Andrews University

Modeling Financial Time Series with S-PlusE. Zivot and J. Wang, 2003New York, Springerxx + 632 pp., £42.00ISBN 0-387-95549-6

Is this yet another entrant to the soon overcrowdedmarket for books on financial econometrics? In thecase of Modeling Financial Time Series with S-Plus,the question is probably less relevant, because thisbook also doubles as an introduction to an S-PLUSmodule for econometric and financial time seriesmodelling, called S+FinMetrics. According to theauthors, their intended audience includes research-ers, practitioners and advanced students of financialmarkets. The book therefore has two aims. It is tobe used both as a text-book and reference work onsome models of financial econometrics and also asa user’s manual for S+FinMetrics. Both aims aremet in these 650 pages.

The first two chapters of the book discuss theuse of S-PLUS, especially in relation to time se-ries. It is, however, assumed that the reader is famil-iar with the S-PLUS language, since only some spe-cial topics are covered. The contents of the restof the book are mostly familiar from other recentbooks on financial econometrics. Chapter 3 is abrief review of the basics of time series analysis.Chapter 4 has a short but very readable discus-sion of unit root and stationarity tests. Chapter 5deals with extreme values, discussing several rele-vant distributions, as well as their estimation andapplication in risk management. Time series regres-sion modelling is considered in Chapter 6. Chap-ter 7 reviews the univariate generalized autoregres-sive conditional heteroscedastic (GARCH) modeland some of its more popular extensions. Fractional

integration long memory models are the subject ofChapter 8.

Chapter 9 discusses rolling analysis of time se-ries (i.e. using a moving sample) and also very brieflysome methods of technical analysis, and Chapter 10looks at systems of regression equations such asseemingly unrelated regressions. Vector autoregres-sive models and co-integration are dealt with inChapters 11 and 12. Chapter 13 is on multivari-ate GARCH models, and Chapter 14 on state spacemodels. Some specific financial models are discussedin the next two chapters: factor models for asset re-turns in Chapter 15 and the modelling of the termstructure of interest rates in Chapter 16. The lastchapter is a brief discussion of outliers and levelshifts in autoregressive integrated moving averagemodels.

Overall, I found that the book has a good bal-ance between theory, S-PLUS code and applica-tions. Not too much space is spent on the technicaldetails of the models. Rather, the emphasis is mostlyon the application of the models, using actual finan-cial data. The examples are on the whole successfulin their task, although some would perhaps havebenefited from a more thorough discussion of theresults. Computational issues, such as the details ofestimation methods, do not receive much attentioneither, which is perhaps somewhat surprising in abook of this type. Exceptions to this can be foundin some comments on the—often very difficult—estimation of fractional integration GARCH andmultivariate GARCH models.

Although the book would not be my first choiceto give to students without access to the relevantsoftware, many of the chapters would certainly beuseful reading for them. Despite their relative brief-ness, the chapters on unit roots and co-integrationfor example are very good discussions of thesetopics. For anyone using S-PLUS for time seriesanalysis this book must be compulsory reading.Practitioners in finance especially should find thebook very useful. More theoretically minded read-ers, however, can probably think of better ways ofspending their money.

Jussi TolviUniversity of Turku