J. H. Albert and S. Chib, Bayesian Analysis of Binary and Polychotomous Response Data, Journal of the American Statistical Association, vol.85, issue.422, pp.669-679, 1993.
DOI : 10.1016/0304-4076(84)90007-1

L. A. Apiolaza, A. R. Gilmour, and D. J. Garrik, Variance modelling of longitudinal height data from a <i>Pinus radiata</i> progeny test, Canadian Journal of Forest Research, vol.30, issue.4, pp.645-654, 2000.
DOI : 10.1139/cjfr-30-4-645

S. A. Ashford and R. R. Swoden, Multi-Variate Probit Analysis, Biometrics, vol.26, issue.3, pp.535-546, 1970.
DOI : 10.2307/2529107

S. Banerjee, B. P. Carlin, and A. E. Gelfand, Hierarchical Modeling and Analysis for Spatial Data, Monographs on Statistics and Applied Probability 101. Boca Raton, FL: Chapman and Hall/CRC. xvii, 452 p, 2004.
DOI : 10.1201/9780203487808

J. E. Bedrick, J. Lapidus, and F. J. Powell, Estimating the Mahalanobis Distance from Mixed Continuous and Discrete Data, Biometrics, vol.15, issue.2, pp.394-401, 2000.
DOI : 10.1111/j.0006-341X.2000.00394.x

F. Caillez, Estimation des volumes et accroissements des peuplements forestiers avec référence particulière aux forêts tropicales, 1980.

B. P. Carlin and N. G. Polsen, Monte Carlo bayesian methods for discrete regression models and categorical time series, Bayesian Statistics Eds, vol.4, pp.577-586, 1993.

B. P. Carlin, N. G. Polsen, and D. S. Stoffer, A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling, Journal of the American Statistical Association, vol.74, issue.418, pp.493-500, 1992.
DOI : 10.1080/01621459.1991.10475006

M. Chen and Q. M. Shao, Properties of Prior and Posterior Distributions for Multivariate Categorical Response Data Models, Journal of Multivariate Analysis, vol.71, issue.2, pp.277-296, 1999.
DOI : 10.1006/jmva.1999.1846

S. Chib and E. Greenberg, Analysis of multivariate probit models, Biometrika, vol.85, issue.2, pp.347-361, 1998.
DOI : 10.1093/biomet/85.2.347
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.8876

M. K. Cowles and B. P. Carlin, Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review, Journal of the American Statistical Association, vol.90, issue.434, pp.883-904, 1996.
DOI : 10.1080/01621459.1996.10476956

D. R. Cox and N. Reid, A note on pseudolikelihood constructed from marginal densities, Biometrika, vol.91, issue.3, pp.729-737, 2004.
DOI : 10.1093/biomet/91.3.729

C. Daganzo, Multinomial Probit. The Theory and its Application to Demand Forecasting Economic Theory, Econometrics, and Mathematical Economics, 1979.

A. R. De-leon, Pairwise likelihood approach to grouped continuous model and its extension, Statistics & Probability Letters, vol.75, issue.1, 2005.
DOI : 10.1016/j.spl.2005.05.017

P. J. Diggle, P. J. Heagerty, K. Y. Liang, and S. L. Zeger, Analysis of Longitudinal Data., Biometrics, vol.53, issue.2, 2003.
DOI : 10.2307/2533983

D. Epron, Y. Nouvellon, O. Roupsard, W. Mouvondy, A. Mabiala et al., Spatial and temporal variations of soil respiration in a Eucalyptus plantation in Congo, Forest Ecology and Management, vol.202, issue.1-3, pp.149-160, 2006.
DOI : 10.1016/j.foreco.2004.07.019

L. Fahrmeir, Posterior Mode Estimation by Extended Kalman Filtering for Multivariate Dynamic Generalized Linear Models, Journal of the American Statistical Association, vol.82, issue.418, pp.501-509, 1992.
DOI : 10.1080/01621459.1988.10478582

A. E. Gelfand and A. F. Smith, Sampling-Based Approaches to Calculating Marginal Densities, Journal of the American Statistical Association, vol.4, issue.410, pp.398-409, 1990.
DOI : 10.1080/01621459.1986.10478240
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.512.2330

A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, Bayesian Data Analysis, 2004.

J. Geweke, Efficient Simulation from the Multivariate Normal and Student t Distributions Subject to Linear Constraints, Computing Science and Statistics: Proceedings of the Twenty Third Symposium on the Interface, pp.571-578, 1991.

W. K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, issue.1, pp.97-109, 1970.
DOI : 10.1093/biomet/57.1.97
URL : http://biomet.oxfordjournals.org/cgi/content/short/57/1/97

H. Joe, Multivariate Models and Dependence Concepts. Monographs on Statistics and Applied Probability. 73. London: Chapman and Hall. xviii, 399 p, 1997.

G. Kauermann, Modeling Longitudinal Data with Ordinal Response by Varying Coefficients, Biometrics, vol.50, issue.3, pp.692-698, 2000.
DOI : 10.1111/j.0006-341X.2000.00692.x

S. Kotz, N. Balakrishnan, and N. L. Johnson, Models and applications, Continuous Multivariate Distributions, vol.1, p.722, 2000.

E. Lesaffre and G. Molenberghs, Multivariate probit analysis: A neglected procedure in medical statistics, Statistics in Medicine, vol.42, issue.9, pp.1391-1403, 1991.
DOI : 10.1002/sim.4780100907

L. Liu and D. Hedeker, A Mixed-Effects Regression Model for Longitudinal Multivariate Ordinal Data, Biometrics, vol.43, issue.1, pp.261-268, 2006.
DOI : 10.1111/j.1541-0420.2005.00408.x

R. Lord, Applications of Item Response Theory to Practical Testing Problems, Erlbaum , Hillside, N.J, 1980.

C. E. Mcculloch and S. R. Searle, Generalized, Linear and Mixed Models, Series in Probability and Statistics, 2001.

F. Mortier, S. Robin, S. Lassalvy, C. P. Baril, and A. , Bar-Hen. Prediction of Euclidean distances with discrete and continuous outcomes, Journal of Multivariate Ananlysis, 2005.

S. M. O-'brien and D. B. Dunson, Bayesian Multivariate Logistic Regression, Biometrics, vol.42, issue.3, pp.739-746, 2004.
DOI : 10.1111/j.0006-341X.2004.00224.x

R. Rekayaa, D. Gianola, and G. Shookb, Longitudinal random effects models for genetic analysis of binary data with application to mastitis in dairy cattle, Genetics Selection Evolution, vol.35, issue.6, pp.457-468, 2003.
DOI : 10.1186/1297-9686-35-6-457

D. Rivers and Q. Vuong, Model selection tests for nonlinear dynamic models, The Econometrics Journal, vol.34, issue.1, pp.1-10, 2002.
DOI : 10.1111/1468-0262.00152

L. Saint-andré, J. Laclau, J. Bouillet, P. Deleporte, A. Miabala et al., Integrative modelling approach to assess the sustainability of the eucalyptus plantations in Congo, In: Connection between Forest Resources and Wood Quality: Modelling Approaches and Simulation Software.IUFRO Working Party S5.01.04, pp.611-621, 2002.

L. Saint-andré, A. Thongo, A. Mabiala, W. Mouvondy, C. Jourdan et al., Age-related equations for above- and below-ground biomass of a Eucalyptus hybrid in Congo, Forest Ecology and Management, vol.205, issue.1-3, pp.199-214, 2005.
DOI : 10.1016/j.foreco.2004.10.006

C. Sicard, L. Saint-andré, D. Gelhaye, and J. Ranger, Effect of initial fertilisation on biomass, nutrient content and nutrient-use efficiency of even-aged Norway spruce and Douglas-fir stands planted in the same ecological conditions. Trees Structure and Function, 2006.

R. Thomas, . Ten, A. Have, and . Morabia, Mixed effects models with bivariate and univariate association parameters for longitudinal bivariate binary response data, Biometrics, vol.55, issue.1, pp.85-93, 1999.

D. Zhang, Generalized Linear Mixed Models with Varying Coefficients for Longitudinal Data, route des Lucioles -BP 93 -06902 Sophia Antipolis Cedex (France) Unité de recherche INRIA Futurs : Parc Club Orsay Université -ZAC des Vignes 4, rue Jacques Monod -91893 ORSAY Cedex, pp.8-15, 2004.
DOI : 10.1093/biostatistics/4.1.57

I. Unité-de-recherche and . Lorraine, Technopôle de Nancy-Brabois -Campus scientifique 615, rue du Jardin Botanique -BP 101 -54602 Villers-lès-Nancy Cedex (France) Unité de recherche INRIA Rennes : IRISA, Campus universitaire de Beaulieu -35042 Rennes Cedex (France) Unité de recherche INRIA Rhône-Alpes : 655, avenue de l'Europe -38334 Montbonnot Saint-Ismier (France) Unité de recherche INRIA Rocquencourt, Domaine de Voluceau -Rocquencourt -BP 105 -78153 Le Chesnay Cedex

I. De-voluceau-rocquencourt, BP 105 -78153 Le Chesnay Cedex (France) http://www.inria.fr ISSN, pp.249-6399