D. Citer-ce,

V. Georgescu, N. Desassis, S. Soubeyrand, A. Kretzschmar, and R. Senoussi, An automated MCEM algorithm for hierarchical models with multivariate and multitype response variables, Communications in Statistics -Theory and Methods, vol.43, issue.17, pp.3698-3719, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01115524

, Comment citer ce document

V. Georgescu, N. Desassis, S. Soubeyrand, A. Kretzschmar, and R. Senoussi, An automated MCEM algorithm for hierarchical models with multivariate and multitype response variables, Communications in Statistics -Theory and Methods, vol.43, issue.17, pp.3698-3719, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01115524

, Comment citer ce document

V. Georgescu, N. Desassis, S. Soubeyrand, A. Kretzschmar, and R. Senoussi, An automated MCEM algorithm for hierarchical models with multivariate and multitype response variables, Communications in Statistics -Theory and Methods, vol.43, issue.17, pp.3698-3719, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01115524

, Comment citer ce document

V. Georgescu, N. Desassis, S. Soubeyrand, A. Kretzschmar, and R. Senoussi, An automated MCEM algorithm for hierarchical models with multivariate and multitype response variables, Communications in Statistics -Theory and Methods, vol.43, issue.17, pp.3698-3719, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01115524

J. References-aitchison and C. H. Ho, The multivariate Poisson log-normal distribution, Biometrika, vol.76, pp.643-653, 1989.

J. G. Booth and J. P. Hobert, Standard errors of prediction in generalized linear mixed models, J. Am. Stat. Assoc, vol.93, pp.262-272, 1998.

J. G. Booth and J. P. Hobert, Maximizing Generalized Linear Mixed Model likelihoods with an automated Monte Carlo EM algorithm, J. Roy. Stat. Soc. B, vol.61, pp.265-285, 1999.

N. E. Breslow and D. G. Clayton, Approximate inference in generalized linear mixed models, J. Am. Stat. Assoc, vol.88, pp.9-25, 1993.

P. Chagneau, F. Mortier, N. Picard, and J. Bacro, A hierarchical bayesian model for spatial prediction of multivariate non-Gaussian random fields, Biometrics, 2010.
URL : https://hal.archives-ouvertes.fr/hal-02643809

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood for incomplete data via the EM algorithm, J. Roy. Stat. Soc. B, vol.39, pp.1-38, 1977.

M. Evans and T. B. Swartz, Bayesian integration using multivariate Student importance sampling, Comp. Sci. Stat, vol.27, pp.456-461, 1996.

C. Fraley and A. E. Raftery, Model-Based Clustering, Discriminant Analysis, and Density Estimation, J. Am. Stat. Assoc, vol.97, pp.611-631, 2002.

E. Guzmán-novoa, L. Eccles, Y. Calvete, J. Mcgowan, P. G. Kelly et al., Varroa destructor is the main culprit for the death and reduced populations of overwintered honey bee (Apis mellifera) colonies in Ontario, Canada. Apidologie, 2010.

X. Lai and K. K. Yau, Long-term survivor model with bivariate random effects: Applications to bone marrow transplant and carcinoma study data, Stat. Med, vol.27, pp.5692-5708, 2008.

C. E. Mcculloch and S. R. Searle, General, Linear and Mixed Models, 2001.

G. J. Mclachlan and T. Krishnan, The EM Algorithm and Extensions. Second Edition, 2008.

V. Georgescu, N. Desassis, S. Soubeyrand, A. Kretzschmar, and R. Senoussi, An automated MCEM algorithm for hierarchical models with multivariate and multitype response variables, Communications in Statistics -Theory and Methods, vol.43, issue.17, pp.3698-3719, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01115524

, Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Manuscrit d'auteur / Author manuscript Version définitive du manuscrit publiée dans / Final version of the manuscript published in, Communications in Statistics Theory and Methods, 2012.

A. M. Tanner, Tools for Statistical Inference: Observed Data and Data Augmentation Methods, 1991.

R. Tunaru, Hierarchical bayesian models for multiple count data, Austrian Journal of Statistics, vol.31, pp.221-229, 2002.

K. Wang, K. K. Yau, A. H. Lee, and G. J. Mclachlan, Two-component Poisson mixture regression modelling of count data with bivariate random effects, Math. Comput. Model, vol.46, pp.1468-1476, 2007.

G. C. Wei and M. A. Tanner, A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms, J. Am. Stat. Assoc, vol.85, pp.699-704, 1990.