Christian P. Robert - Academia Europaea€¦ · Robert, C.P. A whistle-stop tour of Statistics by...
Transcript of Christian P. Robert - Academia Europaea€¦ · Robert, C.P. A whistle-stop tour of Statistics by...
Christian P. Robert
Selected publications/preprints
2014
Banterlé, M., Grazian, C. and Robert, C.P. Accelerating Metropolis-Hastings
algorithms: Delayed acceptance with prefetching. Available
as arxiv:1406.2660
Gelman, A. and Robert, C.P. Revised evidence for statistical
standards. Proc. National Academy Sciences 111(19) E1935
Marin, J.-M., Pillai, N., Robert, C.P. and Rousseau, J. Relevant statistics for
Bayesian model choice. J. Royal Statistical Soc. Series B 76, . Available
as arxiv:1110.4700
Moores, M.T., Drovandi, C., Mengersen, K.L., and Robert, C.P. Pre-
processing for approximate Bayesian computation in image
analysis. Statistics and Computing, 25(1) Available asarxiv:1403.4359
Moreno, E., Vázquez–Polo, F.J., and Robert, C.P. Two discussions of the
paper “Bayesian measures of model complexity and fit" by D. Spiegelhalter
et al. J. Royal Statistical Soc. Series B 76(3), 486. Available
as arxiv:1310.2905
Kamari, K. and Robert, C.P. Reflecting about Selecting Noninformative
Priors. Available as arxiv:1402.6257
Pudlo, P., Marin, J.-M., Estoup, A., Cornuet, J.-M., Gauthier, M. and Robert,
C.P. Reliable ABC model choice via random forests. Available
as arXiv:1406.6288
Robert, C.P. On the Jeffreys-Lindley paradox. Philosophy of Science 81,
216-232. Available as arxiv:1303.5973
Robert, C.P. Des spécificités de l'approche bayésienne et de ses justifications
en statistique inférentielle. In Les approches et méthodes
bayésiennes, sciences et épistémologie (ed. I. Drouet), Éditions
Matériologiques (to appear). Available as arxiv:1403.4429
Discussion of "Deviance Information Criterion" by D. J. Spiegelhalter, N. G.
Best, B. P. Carlin and A. van der Linde. J. Royal Statistical Society, Series
B 76(3), 492.
Robert, C.P. Medical Illuminations: Using Evidence, Visualization and
Statistical Thinking to Improve Healthcare by H. Wainer CHANCE 27(3),
57-58.
Robert, C.P. Statistical Modeling and Computation by D. Kroese and J.
Chen CHANCE 27(2), 61-62.
Robert, C.P. Machine Learning, A Probabilist Perspective by K.
Murphy CHANCE 27(2), 62-63.
Robert, C.P. Statistics for Spatio-Temporal Data by N. Cressie and C.
Wikle CHANCE 27(2), 64.
Robert, C.P. The Cartoon Guide to Statistics by G. Klein and A.
Dabney CHANCE 27(1), 61.
Robert, C.P. Naked Statistics by C. Wheelan CHANCE 27(1), 58-59.
Robert, C.P. The Most Human Human by B. Christian CHANCE 27(1), 57.
Robert, C.P. Bayesian Data Analysis by A. Gelman et al. J. American
Statist. Assoc. 109(507), 1326-1327
Robin, A.C., Reyle, C., Fliri, J., Czekaj, M., Robert, C.P. and Martins, A. M.
M. Constraining the thick disc formation scenario of the Milky
Way. Astronomy & Astrophysics 569, A13. Available asarXiv:1406.5384
Salmeron, D., Cano, J.A., and Robert, C.P. Objective Bayesian hypothesis
testing in binomial regression models with integral prior
distributions. Statistica Sinica (to appear). Available
asarxiv.org/abs/1306.6928.
2013
Atchadé, Y., Lartillot, N., and Robert, C.P. Bayesian computation for
intractable normalizing constants. Brazilian Journal of Statistics 27(3), 417-
436. Available as arXiv:0804.3152
Chopin, N., Gelman, A., Mengersen, K. and Robert, C.P. In praise of the
referee. ISBA Bulletin 20(1), 13-18. Available as arXiv:1205.4304
Gelman, A. and Robert, C.P. ―Not only defended but also applied‖: The
perceived absurdity of Bayesian inference (with discussion). The American
Statistician 67(1), 1-5. Available asarXiv:1210.7225
Gelman, A., Robert, C.P., and Rousseau, J. Inherent difficulties of non-
Bayesian likelihood-based inference, as revealed by an examination of a
recent book by Aitkin. Statistics & Risk Modeling 30, 1001-1016. Available
as arxiv.org/pdf/1012.2184
Lee, K. and Robert, C.P. Importance sampling schemes for evidence
approximation in mixture models. Available as arxiv:1311.600
Marin, J.-M. and Robert C.P. Bayesian Essentials with R. Springer-Verlag,
New York.
Mengersen, K., Pudlo, P., and Robert, C.P. Bayesian computation via
empirical likelihood. Proceedings of the National Academy of
Sciences 110 (4), 1321–1326. Available asarXiv:1205.5658
Robert, C.P. Error and inference: an outsider stand on a frequentist
philosophy. Theory and Decision 74, Issue 3, 447-461. Available
as arXiv:1111.5827.
Robert, C.P. Bayesian Computational Tools. Annual Review of Statistics and
Its Application / Volume 1, 153-177.
Robert, C.P. R for Dummies by A. de Vries and J. Meys CHANCE 26(4), 61.
Robert, C.P. Magical Mathematics: The Mathematical Ideas that Animate
Great Magic Tricks by P. Diaconis and R. Graham CHANCE 26(2), 50-51.
Robert, C.P. Paradoxes in Statistical Inference by M.
Chang CHANCE 26(2), 52-54.
Robert, C.P. In Pursuit of the Unknown: 17 Equations that Chnged the
World by I. Stewart CHANCE 26(2), 54-58.
Robert, C.P. Guesstimation by L. Weinstein and J.A. Adam and
Guesstimation 2.0 by L. Weinstein CHANCE 26(2), 58-59.
2012
Celeux, G., El Anbari, M., Marin, J.-M., and Robert, C.P. Regularization in
Regression: Comparing Bayesian and Frequentist Methods in a Poorly
Informative Situation. Bayesian Statistics 7(2), 477-502 (available on-line).
Cornuet, J.M., Marin, J.-M., Mira, A. and Robert, C.P. Adaptive Multiple
Importance Sampling. Scandinavian Journal of Statistics 39(4), 798--812.
Available as arXiv:0907.1254
Estoup, A., Lombaert, E., Marin, J.-M., Guillemaud, T., Pudlo, P., Robert,
C.P., and Cornuet, J.-M. Estimation of demo-genetic model probabilities
with Approximate Bayesian Computation using linear discriminant analysis
on summary statistics. Molecular Ecology Ressources 12(5), 846--855.
Available as pdf file.
Robert, C.P. The theory that would not die, by Sharon Bertsch
McGrayne. CHANCE 25(1), 49-50. Available as pdf file.
Robert, C.P. The cult of significance, by Stephen Ziliak and Deirdre
McCloskey. CHANCE 25(1). 51-53 Available as pdf file.
Robert, C.P. Handbook of Markov chain Monte Carlo, edited by Steve
Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng. CHANCE 25(1).
53-55.Available as pdf file.
Robert, C.P. Handbook of fitting statistical distributions with R by Z. Karian
and E.J. Dudewicz: CHANCE 25(1), 56-57. Available as pdf file.
Robert, C.P. Bayesian modeling using WinBUGS by Ioannis Ntzoufras.
CHANCE 25(2).Available as pdf file.
Robert, C.P. Bayesian ideas and data analysis by Ronald Christensen,
Wesley Johnson, Adam Branscum, and Timothy Hanson.
CHANCE 25(2).Available as pdf file.
Robert, C.P. Understanding computational Bayesian statistics by William
Boldstad. CHANCE 25(2). Available as pdf file.
Robert, C.P. Principles of Applied Statistics by David Cox and Christl
Donnely. CHANCE 25(3), 58-59.
Robert, C.P. Large-scale inference: Empirical Bayes methods for estimation,
testing, and prediction by Brad Efron. CHANCE 25(3), 59-61.
Robert, C.P. A whistle-stop tour of Statistics by Brian
Everitt. CHANCE 25(3), 61.
Robert, C.P. Correlations between the physical and social sciences by
Valentine Belfiglio. CHANCE 25(3), 62.
Robert, C.P. Principles of Uncertainty by Joseph Kadane. JASA (to appear).
Available as pdf file.
2011
Beffy, M. and Robert, C.P. Discussions of `Riemann manifold Langevin and
Hamiltonian Monte Carlo methods" by Girolami and Calderhead. Journal of
the Royal Statistical Society, Series B,73(2), 173.
Douc, R. and Robert, C.P, A vanilla Rao--Blackwellisation of Metropolis-
Hastings algorithms. Annals of Statistics 39(1), 261-277. Available
as arXiv:0904.2144v2
Jacob, P., Robert, C.P., and Smith, M. Using parallel computation to
improve Independent Metropolis--Hastings based estimation. Journal of
Computational and Graphical Statistics 20(3): 616-635. Available
as arXiv:1010.1595
Hobert, J.P., Roy, V. and Robert, C.P. Improving the Convergence
Properties of the Data Augmentation Algorithm with an Application to
Bayesian Mixture Modeling. Statistical Science 3(2011), 332-351. Available
as pdf file.
Marin, J.-M., Pillai, N., Robert, C.P. and Rousseau, J. Relevant statistics for
Bayesian model choice. Available as arXiv:1110.4700
Marin, J.-M., Pudlo, P., Robert, C.P., and Ryder, R. Approximate Bayesian
Computational methods. Statistics and Computing 21(2), 289-291. Available
as arXiv:1101.0955
Marin, J.-M. and Robert, C.P. Discussions of `Riemann manifold Langevin
and Hamiltonian Monte Carlo methods" by Girolami and
Calderhead. Journal of the Royal Statistical Society, Series B,73(2), 189-
190.
Robert, C.P. An attempt at reading Keynes' Treatise on
Probability. International Statistical Review 79(1), 1-15. Available
as arxiv:1003.4455
Robert, C.P. Evidence and Evolution: A review. Human Genomics 5(2),
130-136)). Available as arXiv:1004.5074
Robert, C.P. Computational Statistics: A review. Statistics and
Computing (to appear). Available as pdf file
Robert, C.P. A Comparison of the Bayesian
and frequentist approaches to estimation: A review. International Statistical
Review 79(1), 117-118. Available as pdf file.
Robert, C.P. Bayesian model selection and statistical modeling: A
review. International Statistical Review 79(1), 120-121. Available as pdf
file.
Robert, C.P. Bayesian decision analysis: A review. International Statistical
Review 79(2), 272–273. Available as pdf file.
Robert, C.P. Time Series: Modeling, Computation, and Inference: A
review. International Statistical Review 79(2), 277–279. Available as pdf
file.
Robert, C.P. A handbook of statistical analyses with R: A
review. International Statistical Review 79(2), 276–277. Available as pdf
file.
Robert, C.P. The foundations of Statistics: a simulation-based approach: A
review. International Statistical Review (to appear). Available as pdf
file.
Robert, C.P. The foundations of Statistics: a simulation-based approach by
Shravan Vasishth and Michael Broe. CHANCE 24(4), 59-60. Available
as pdf file.
Robert, C.P. Numerical Analysis for Statisticians: A review. International
Statistical Review (to appear). Available as pdf file.
Robert, C.P. Numerical Analysis for Statisticians, by Kenneth Lange.
CHANCE 24(4), 58-59. Available as pdf file.
Robert, C.P. Handbook of fitting statistical distributions with R: A
review. International Statistical Review (to appear). Available as pdf
file.
Robert, C.P. Anathem, by Neal Stephenson. CHANCE 24(4), 60-61.
Available as pdf file.
Robert, C.P. Discussion of `Is Bayes Posterior just Quick and Dirty
Confidence?' by D. Fraser. Statistical Science 3(2011), 317-318. Available
as pdf file.
Robert, C.P. Interactive comment on ―DREAM(D): an adaptive Markov
chain Monte Carlo simulation algorithm to solve discrete, noncontinuous,
posterior parameter estimation problems‖ by J. A. Vrugt. Hydrol. Earth Syst.
Sci. Discuss., 8, C1353–C1356.
Robert, C.P. Discussion of `Riemann manifold Langevin and Hamiltonian
Monte Carlo methods" by Girolami and Calderhead. Journal of the Royal
Statistical Society, Series B, 73(2), 168-170.
Robert, C.P. and Casella, G. A History of Markov Chain Monte Carlo-
Subjective Recollections from Incomplete Data. Statistical Science 26(1),
102-115. Available as arXiv0808.2902
Robert, C.P., Cornuet, J.-M., Marin, J.-M. and Pillai, N.S. Lack of
confidence in approximate Bayesian computational (ABC) model
choice. PNAS (Open Access). 108(37), 15112-15117. Available
as arXiv:1102.4432
Robert, C.P., Marin, J.-M. and Pillai, N.S. Why approximate Bayesian
computational (ABC) methods cannot handle model choice problems (earlier
version of the above). Available as arXiv:1101.5091
2010
Barthelmé, S., Beffy, M. Chopin, N., Doucet, A., Jacob, P., Johansen, A.M.,
Marin, J.-M., and Robert, C.P. Discussions of `Riemann manifold Langevin
and Hamiltonian Monte Carlo methods" by Girolami and
Calderhead. Journal of the Royal Statistical Society, Series B (to appear).
Available as arXiv:1011.0834
Beaumont, M.A., Nielsen, R., Robert, C.P., Hey, J., Gaggiotti, O., Knowles,
L., Estoup, A., Mahesh, P., Coranders, J., Hickerson, M., Sisson, S.,
Fagundes, N., Chikhi, L., Beerli, P., Vitalis, R., Cornuet, J.-M.,
Huelsenbeck, J., Foll, M., Yang, Z., Rousset, F., Balding, D. and Excoffier,
L. In defense of model-based inference in phylogeography. Molecular
Ecology 19(3), 436-446.
Berger, J.O., Fienberg, S., Raftery. A. and Robert, C.P. Letter on Incoherent
Phylogeographic Inference. Letter to PNAS, 107(41) E157. Available
as arXiv:1006.3854
Casella, G. and Robert, C.P. Report of the Editors - 2009. J. Royal Statistical
Society Series B, 72(1), 1-2.
Chopin, N., Iacobucci, A., Marin, J.-M., Mengersen, K.L., Robert, C.P.,
Ryder, R. and Schäfer, C. On particle learning (discussions on Lopes et
al.). Bayesian Statistics 9 (to appear). Available asarXiv:1006.0554
Chopin, N. and Robert, C.P. Properties of Nested Sampling. Biometrika 97, 741-
755 doi:10.1093/biomet/asq021. Available as arXiv:0801.3887
Chopin, N. and Robert, C.P. Discussion on Wilkinson's Parameter inference for
stochastic kinetic models of bacterial gene regulation. Bayesian Statistics
9 (to appear). Available as pdf file
Hobert, J.O., Roy, V. and Robert, C.P. Improving the Convergence
Properties of the Data Augmentation Algorithm with an Application to
Bayesian Mixture Modelling. Available asarXiv:0911.4546
Iacobucci, A., Marin, J.-M., and Robert, C.P. On variance stabilisation by
double Rao-Blackwellisation. Computational Statistics and Data
Analysis 54, 698-710. Available as arXiv:0802.3690
Kilbinger, M., Wraith, D., Robert, C.P. , Benabed, K., Cappé, O., Cardoso,
J.-F., Fort, G., Prunet, S., Bouchet, F. Bayesian model comparison in
cosmology with population Monte Carlo. Monthly Notices of the Royal
Astronomical Society: Letters. 405(4), 2381-2390 Available
as arXiv:0912.1614.
Marin, J.-M., and Robert, C.P. On resolving the Savage-Dickey
paradox. Electronic Journal of Statistics 4, 643-654. Available
as arXiv:0910.1452
Marin, J.-M., and Robert, C.P. Importance sampling methods for Bayesian
discrimination between embedded models. In Frontiers of Statistical
Decision Making and Bayesian Analysis (eds., M.-H. Chen, D.K. Dey, P.
Müller, D. Sun, K. Ye). Chapter 14, pages 513-553.
Robert, C.P. A Search for Certainty: A critical assessment. Bayesian
Analysis (with discussion) 05, 02, 213-222. Available
as arXiv:1001.5109
Robert, C.P. Bayesian computational methods.Handbook of Computational
Statistics (Volume I) Concepts and Fundamentals, Chapter III.11. J. Gentle,
W. Härdle, Y. Mori (eds) Springer-Verlag, Heidelberg (second edition).
Available as arxiv:1002.2702
Robert, C.P. and Arbel, J. Discussion on Polson and Scott's Sparse Bayesian
regularization and prediction. Available as pdf file
Robert, C.P. and Casella, G. Introducing Monte Carlo Methods with R:
Solutions to Odd-Numbered Exercises. Available as arXiv:1001.2906
Robert, C.P. and Casella, G. A History of Markov Chain Monte Carlo-
Subjective Recollections from Incomplete Data. In Handbook of Markov
Chain Monte Carlo: Methods and Applications, edited by Steve Brooks,
Andrew Gelman, Galin Jones, and Xiao-Li Meng (to appear). Available
as arXiv0808.2902
Robert, C.P. and Casella, G. Generating Random Variables" (version
13). StatProb: The Encyclopedia Sponsored by Statistics and Probability
Societies.
Robert, C.P. and Marin. J.-M. On computational tools for Bayesian analysis.
In Rethinking Risk Measurement and Reporting, vol. 1, 29-68. Edited by K.
Böcker. Available as arxiv:1002.2684
Robert, C.P. and Rousseau, J. On Bayesian data analysis. In Rethinking Risk
Measurement and Reporting, vol. 1, 3-28. Edited by K. Böcker. Available
as arxiv:1001.4656
Robert, C.P. and Rousseau, J. Discussion on Bernardo's Integrated objective
Bayesian estimation and hypothesis testing. Bayesian Statistics 9 (to appear).
Available as pdf file
Rousseau, J. and Robert, C.P. Discussion on Consonni and LaRocca's On
moment priors for Bayesian model choice. Bayesian Statistics 9 (to appear).
Available as pdf file
2009
Beaumont, M., Robert, C.P., Marin, J.-M. and Cornuet, J.M. Adaptivity for
ABC algorithms: the ABC-PMC scheme. Biometrika 96(4), 983-990.
Available as arXiv:08052256
Cucala, J., Marin, J.-M., Robert, C.P. and Titterington, D.M. A Bayesian
reassessment of nearest--neighbour classification. Journal of the American
Statistical Association, March 1, 2009,104(485): 263-273. Available
as doi:10.1198/jasa.2009.0125 | arXiv:0802.1357 | pdf file
Grelaud, A., Marin, J.-M., and Robert, C.P, ABC methods for model choice
in Gibbs random fields, Notes aux Comptes Rendus de l'Académie des
Sciences 347(3-4), 205-210.
Grelaud, A., Marin, J.-M., Robert, C.P., Rodolphe, F. and Tally, F.
Likelihood-free methods for model choice in Gibbs random fields. Bayesian
Analysis, 3(2), 427-442 . Revised version available as arXiv:0807.2767
Jacob, P., Chopin, N., Robert, C.P., and Rue, H. Comments on "Particle
Markov chain Monte Carlo methods" by Andrieu, Doucet and Hollenstein.
Journal of the Royal Statistical Society (to appear). Available
as arXiv:0911.0985
Lee, K., Mengersen, K.L., Marin, J.-M., and Robert, C.P. Bayesian Inference
on Mixtures of Distributions. Perspectives in Mathematical Sciences. Stat.
Sci. Interdiscip. Res., 7, 165-202. World Sci. Publ., Hackensack, NJ.
Available as arXiv:0804.2413
Marin, J.-M and Robert, C.P., Les bases de la statistique
bayésienne, Techniques de l'Ingénieur. AF 605. Earlier version available
as pdf file
Robert, C.P Monte Carlo methods in Statistics. Available
as arXiv:0909.0389
Robert, C.P On the relevance of the Bayesian approach to Statistics. Review
of Economic Analysis (to appear). Available as arXiv:0909.5369
Robert, C.P Discussion of "Natural Induction: An objective Bayes approach"
by Berger, Bernardo and Sun, Revista de la Real Academia of Ciencias,
Series A Matemáticas) (to appear).
Robert, C.P. and Casella. G. Introducing Monte Carlo Methods with R. Use
R! Springer Verlag, New York.
Robert, C.P., Chopin, N. and Rousseau, J. Harold Jeffreys' Theory of
Probability revisited (with discussion). Statistical Science 24(2), 141-172
and 191-194 (reply to the discussion). Available as arXiv:0804.3173 and
as arXiv:0909.1008 (reply to the discussion).
Robert, C.P. and Marin, J.-M. Bayesian Core: The Complete Solution
Manual. Available as arXiv:0910.4696
Robert, C.P., Mengersen, K.L., and Chen, C. Model choice versus model
criticism. Letter to PNAS (doi:10.1073/pnas.0911260107) 107(3), E5.
Available as arXiv:0909.5673
Robert, C.P. and Wraith, D., Computational methods for Bayesian model
choice. AIP Proceedings, Volume 1193, pp. 251-262 Bayesian Inference
and maximum entropy methods in Science and Engineering: The 29th
International Workshop on Bayesian Inference and Maximum Entropy
Methods in Science and Engineering; doi:10.1063/1.3275622. Available
as arXiv:0907.5123.
Wraith, D., Kilbinger, M., Benabed, K., Cappé, O., Cardoso, J.-F., Fort, G.,
Prunet, S., Robert, C.P. Estimation of cosmological parameters using
adaptive importance sampling. Physical Review D,80, 023502. Available
as arXiv:0903.0837
2008
Ben Mansour, S, Jouini, E., Marin, J.-M., Napp, C. and Robert, C.P. Are
risk agents more optimistic? A Bayesian estimation approach. Journal of
Applied Econometrics 23(6), 843-860.
Cano, J.A., Salmeron, D. and Robert, C.P. Integral equation solutions as
prior distributions for Bayesian model selection. TEST 17(3), 493-504.
Available as pdf file
Cappé, O., Douc, R., Gullin, A., Marin, J.-M. and Robert, C.P. Adaptive
Importance Sampling in General Mixture Classes. Statistics and
Computing 18, 447-459. Available as arXiv:0710.4242v1| pdf file
Casarin, R. and Robert, C.P. Discussion of "Approximate Bayesian inference
for latent Gaussian models by using integrated nested Laplace
approximations” by Rue, Martino, and Chopin.Journal of the Royal
Statistical Society pdf file.
Casella, G. and Robert, C.P. Report of the Editors — 2008. Journal of the
Royal Statistical Society pdf file. Chopin, N. and Robert, C.P. Contemplating Evidence: properties, extensions of, and
alternatives to Nested Sampling. Programs available
as progs.nc.tar.gz and progs.cpr.tar.gz. Revised version available asarXiv:0801.3887 Cornuet, J.M., Santos, F., Beaumont, M.A., Robert, C.P., Marin, J.-M.,
Balding, D.A., Guillemaud, T. and Estoup, A. Infering population history
with DIY ABC: a user-friendly approach to Approximate Bayesian
Computation. Bioinformatics 24(23), 2713-2719. Available
as arXiv:0804.4372 | pdf file
Marin, J.-M, Casarin, R. and Robert, C.P., Discussion of "Approximate
Bayesian inference for latent Gaussian models by using integrated nested
Laplace approximations” by Rue, Martino, and Chopin. Journal of the
Royal Statistical Society
Marin, J.-M and Robert, C.P., Approximating the marginal likelihood in
mixture models. Bulletin of the Indian Chapter of ISBA V(1), 2-7. Available
as arXiv0804.2414 | pdf file
Robert, C.P. Discussion of "Sure independence screening for ultra-high
dimensional feature space" by Fan and Lv. Journal of the Royal Statistical
Society 70(5), 901. pdf file.
Robert, C.P. Discussion of "Approximate Bayesian inference for latent
Gaussian models by using integrated nested Laplace approximations” by
Rue, Martino, and Chopin. Journal of the Royal Statistical Society pdf file.
Robert, C.P. À propos de l'article de N. Vayatis "Bayésiens contre
fréquentistes, un faux débat". La Recherche 424, 6.
Robert, C.P. A message from the president. ISBA
Bulletin 15(1), 15(2), 15(3), 15(4)
Robert, C.P. Misconceptions on Bayesianism. ISBA Bulletin 15(4), 2-3.
Robert, C.P. and Marin, J.-M., Some difficulties with some posterior
probability approximations. Bayesian Analysis 3(2), 427-442. Available
as arXiv:0801.3513
2007
Alston, C.L., Mengersen, K.L, Robert, C.P., Thompson, J.M., Littlefield, P.J.
and Ball, A.J. Bayesian mixture models in a longitudinal setting for
analysing sheep CAT scan images.Computational Statistics and Data
Analysis, 51(9), 4282-4296.
Cappé, O. and Robert, C.P. Une approche Monte Carlo adaptative pour
l’approximation de lois a posteriori avec application à l’inférence de
paramètres cosmologiques. Proceedings, GRETSI, Troyes. Available as pdf
file Chopin, N. and Robert, C.P. Contemplating Evidence: properties, extensions of, and
alternatives to Nested Sampling. Available as arXiv:0801.3887 | pdf file
Douc, R., Guillin, A., Marin, J.M., and Robert, C.P., Minimum variance
importance sampling via population Monte Carlo. ESAIM Probability and
Statistics 11, 427-447. Available as pdf
Douc, R., Guillin, A., Marin, J.-M. and Robert, C.P. Convergence of
adaptive mixtures of importance sampling schemes, Annals of
Statistics, 35(1), 420-448. Available as pdf|Snw
Kendall, W.S., Marin, J.-M. and Robert, C.P. Confidence bands for
Brownian motion and applications to Monte Carlo simulations, Statistics and
Computing , 17(1) 1-10. Available as pdf file
Marin, J.-M. and Robert, C.P., Bayesian Core: A Practical Approach to
Computational Bayesian Statistics, Springer-Verlag, New York [webpage].
Robert, C.P. The Bayesian Choice. Paperback edition, Springer-Verlag.
Robert, C.P., Discussion of Jain and Neal's ``Splitting and merging
components of a nonconjugate Dirichlet process mixture model". Bayesian
Analysis. Available as pdf file
2006
Amzal, B., Bois, F.Y., Parent, E. and Robert, C.P. Bayesian optimal design
via interacting MCM. J. American Statist. Assoc. 101, 773-785. Available
as Postscript file.
Celeux, G., Marin, J.-M., and Robert, C.P., Sélection bayésienne de
variables en régression linéaire. Journal de la Société Française de
Statistique, 147, 1, 59-79. Available as pdf file
Celeux, G., Marin, J.M. and Robert, C.P. Iterated importance sampling in
missing data problems. Computational Statistics and Data Analysis 50(12)
3386-3404. Available as PDF file.
Chopin, N. and Robert, C.P., A discussion of John Skilling's Nested
sampling for the Valencia 8 Meeting. Available as pdf file. Reply from the
author edited here
Müller, P., Robert, C.P. and Rousseau, J., Sample Size Choice for
Microarray Experiments In Bayesian Inference for Gene Expression and
Proteomics (eds. K.A. Do, P.Müller and M.Vannucci). Cambridge
University Press.
Robert, C.P., Le Choix Bayésien : Principes et implémentation Springer-
Verlag, Paris. [Springer order]
Robert, C.P., "A review of Gaussian Markov Random Fields (Theory and
Applications) by Håvard Rue and Leonhard Held", Statistics in Medicine (to
appear).
Robert, C.P., Three discussions on Bayesian model choice. Cahiers du
Ceremade 2006-2. Available as pdf file
2005
Celeux, G., Forbes, F., Robert, C.P. and Titterington, D.M. Deviance
information criteria for missing data models (with discussion) Bayesian
Analysis 1(4), 651-674. Available as PDF file|R program|dataset1|dataset2
Guillin, A., Marin, J.M. and Robert, C.P. Estimation bayesienne
approximative par echantillonnage preferentiel. Revue de Statistique
Appliquée LIII, 1, 79-95 and Cahiers du Ceremade 0335. Available as PDF
file.
Hobert, J.P., Jones, G.L. and Robert, C.P. Using a Markov chain to construct
a tractable approximation of an untractable probability
distribution. Scandinavian Journal of Statistics et Cahiers du
Ceremade 0403. Available as PDF file.
Marin, J.M., Mengersen, K. and Robert, C.P. Bayesian modelling and
inference on mixtures of distributions. Handbook of Statistics 25, D. Dey
and C.R. Rao (eds). Elsevier-Sciences). Available asPDF file.
2004
Andrieu, C., Doucet, A. and Robert, C.P. Computational Advances for and
from Bayesian Analysis. Statistical Science 19(1), 120-129. Available
as PDF file.
Cappé, O., Guillin, A., Marin, J.M., and Robert, C.P., Population Monte
Carlo. J. Comput. Graph. Stat. 13(4), 907-929 Available as Gzipped
postscript.
Casella, G., Robert, C.P. and Wells, M.T., Mixture models, latent variables
and partitioned importance sampling. Statist. Method. 1(1), 1-18.
Hobert, J.P. and Robert, C.P. A Mixture Representation of pi with
Applications in Markov Chain Monte Carlo and Perfect Sampling. Annals of
Applied Proba. 14(3), 1295-1305. Available asCompressed postscript.
Kendall, W.S., Marin, J.M., and Robert, C.P. Brownian confidence bands on
Monte Carlo output. Cahiers du Ceremade. Available as PDF file.
Muller, P., Parmigiani, G., Robert, C.P. and Rousseau, J. Optimal Sample
Size for Multiple Testing: the Case of Gene Expression Microarrays. J.
American Stat. Assoc. 99, 990-1001. [Gzipped postscript|Slides]
Robert, C.P. Discussion on the Inverse Problem half-day. J. Royal Statis.
Society. (to appear) [Slides|Written]
Robert, C.P. Bayesian computational methods. Handbook of Computational
Statistics (Volume I) Concepts and Fundamentals, Chapter III.11. J. Gentle,
W. Härdle, Y. Mori (eds) Springer-Verlag, Heidelberg . Available as PDF
file.
Robert, C.P. and Casella, G. Monte Carlo Statistical Methods. Springer-
Verlag, New York.
2003
Cappé, O., Robert, C.P., and Rydén, T. Reversible jump MCMC converging to
birth-and-death MCMC and more general continuous time samplers. J. Royal
Statis. Society Series B 65(3), 679-700. Available as Gzipped postscript.
Dupuis, J.A. and Robert, C.P. Bayesian variable selection in qualitative models
by Kullback-Leibler projections. In J. Statistical Planning and Inference 111, 77-
94. Available asPostscript.
Hurn, M., Justel, A. and Robert, C.P. Estimating mixtures of regressions. J.
Comput. Graph. Stat. 12(1), 1-25. Available as [Compressed postscript | pdf].
Mengersen, K.L. and Robert, C.P. The pinball sampler. Bayesian Statistics
7 (edited by J.M. Bernardo, A.P. Dawid, J.O. Berger, and M. West) [Compressed
postscript]
Philippe, A. and Robert, C.P. Perfect simulation of positive Gaussian
distributions. Statistics and Computing 13(2), 179-186. [Compressed postscript]
Robert, C.P. Discussion of Kong, McCullagh, Nicolae, Tan, and Meng. J. Royal
Statis. Society. 65(3), 606-609. [Slides|Written]
Robert, C.P. Discussion of Brooks, Giudici and Roberts, J. Royal Statis.
Society. 65(1), 39-42 Gzipped postscript.
Robert, C.P. and Rousseau, J. A mixture approach to Bayesian goodness of fit
(revised version). Available as PDF file.
2002
Casella, G., Mengersen, K.L., Robert, C.P., and Titterington, D.M. Perfect
Slice Samplers for Mixtures of Distributions. J. Royal Statis. Society Series
B 64(4), 777-790. Available as Compressed postscript.
DeIorio, M. and Robert, C.P., Discussion of Spiegelhalter et al., J. Royal
Statis. Society Series B 64(4), 629-630. Available as Gzipped postscript.
Douc, R., O. Cappé, E. Moulines, and C. P. Robert. On the Convergence of
the Monte Carlo Maximum Likelihood Method for Latent Variable
Models. Scandinavian J. Statist. 29(4), 615-636. [Abstract][Compressed
postscript]
Doucet, A., Godsill, J.A. and Robert, C.P. Marginal maximum a posteriori
estimation using Markov chain Monte Carlo. Statistics and Computing 12,
77-84 [Compressed postscript]
Marin, J.-M. and Robert, C.P. (2002) Discussion on a paper of S. L.
Lauritzen and T. S. Richardson: Chain graph models and their causal
interpretation,
J. Royal Statis. Society Series B, 64, 3
Robert, C.P. A review of Finite Mixture Models by G. McLachlan and D.
Peel. J. American Statist. Assoc. (It actually never appeared!).
Robert, C.P. and Rousseau, J. A Mixture Approach to Bayesian Goodness
of Fit. Cahier du CEREMADE 02009. Available as Gzipped postscript.
Robert, C.P. and Titterington, D.M. Discussion of Spiegelhalter et al., J.
Royal Statis. Society Series B 64(4), 621-622. Available as Gzipped
postscript.
2001
Altaleb, A. and Robert, C.P. Analyse bayesienne du modele Logit :
algorithme par tranches ou Metropolis-Hastings ? Revue de Statistique
Appliquée 49, 53-70.
Andrieu, Ch., and Robert, C.P. Controlled MCMC for Optimal Sampling.
Available as Gzipped postscript.
Casella, G., Lavine, M. and Robert, C.P. Explaining the Perfect
Sampler. The American Statistician 55(4), 299-305. Available
as Compressed pdf file.
Philippe, A. and Robert, C.P. Riemann sums for MCMC estimation and
convergence monitoring. Statistics and Computing 11, 103-115.
Robert, C.P. The Bayesian Choice. second edition, Springer-Verlag.
2000
Cappé, O. and Robert, C.P. Ten years and still running! J. American Statist.
Assoc. 95 (4), 1282-1286. Available as html file.
Casella, G., Robert, C.P. and Wells, M.T. Rao-Blackwellization of
Generalized Accept-Reject Schemes. Tech. Report, Dept. of Statistics, UFL.
Available as Compressed postscript.
Casella, G., Robert, C.P. and Wells, M.T. Mixture models, latent variables
and partitioned importance sampling. Tech. Report DT-2000-03, CREST,
INSEE, Paris. Available as Compressed postscript.
Celeux, G., Hurn, M. and Robert, C.P. Computational and inferential
difficulties with mixture posterior distributions. J. American Statist.
Assoc. 95 957-970.
Doucet, A. and Robert, C.P. Maximum a posteriori parameter estimation for
hidden Markov models. Tech. Report, Signal Processing Group, University
of Cambridge. Available as Compressed postscript.
Fourdrinier, D., Philippe, A. and Robert, C.P. Estimation of a non-centrality
parameter under Stein type like losses J. Statistical Planning and
Inference 87(1), 43-54.
Robert, C.P., Rydén, T. and Titterington, D.M. Bayesian inference in hidden
Markov models through jump Markov chain Monte Carlo J. Royal Statis.
Society Series B 62(1), 57-75.
1999
Billio, M., Monfort, A. Robert, C.P. Bayesian estimation of switching
ARMA models. J. Econometrics, 93 229-255. [Abstract][Full paper] (PDF).
Gruet, M.A., Philippe, A. and Robert, C.P. MCMC Control Spreadsheets for
Exponential Mixture Estimation. J. Comput. Graph. Stat. 8, 298-317. See
also the related software expmix.
Hobert, J. and Robert, C.P. Eaton's Markov chain, its conjugate partner and
P-admissibility. Annals of Statistics 27, 361-373.
Hobert, J., Robert, C.P. and Titterington, D.M. On perfect simulation for
some mixtures of distributions. Statistics and Computing 9 287-298.
Mengersen, K. L., Robert, C.P. and Guihenneuc-Jouyaux, C. MCMC
convergence diagnostics: a "reviewww". In Bayesian Statistics 6 (J. Berger,
J. Bernardo, A.P. Dawid and A.F.M. Smith,