Multivariate dynamic model for ordinal outcomes

Journal of Multivariate Analysis

Volumn 99, Issue 8, 2008, Pages 1717-1732

  Chaubert, F ^a   Mortier, F ^a   Saint Andre L ^a  

^a CIRAD   (France)

Author keywords

92B15; Biomass; Dynamic multivariate ordinal probit model; Latent variable; Longitudinal data; MCMC; Ordinal variable

Indexed keywords

EID: 48549097695     PISSN: 0047259X     EISSN: 10957243     Source Type: Journal    
DOI: 10.1016/j.jmva.2008.01.011     Document Type: Article

References (41)

1. Albert J.H., and Chib S. Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association 88 422 (1993) 669-679
2. Apiolaza L., Gilmour A., and Garrik D. Variance modelling of longitudinal height data from a pinus radiata progeny test. Canadian Journal of Forest Research 30 (2000) 645-654
3. Ashford S., and Swoden R.R. Multivariate probit analysis. Biometrics 26 (1970) 535-546
4. Banerjee S., Carlin B.P., and Gelfand A.E. Hierarchical modeling and analysis for spatial data. Monographs on Statistics and Applied Probability vol. 101 (2004), Chapman and Hall/CRC, Boca Raton, FL xvii, 452 p
5. Bedrick J., Lapidus J., and Powell F. Estimating the mahalanobis distance from mixed continuous and discrete data. Biometrics 56 2 (2000) 394-401
6. F. Caillez, 1980. Estimation des volumes et accroissements des peuplements forestiers avec référence particulière aux forts tropicales, vol. 1 Estimation des volumes, Etude FAO Fort, N22/1, FAO Rome, 99p
7. Carlin B., and Polsen N. Monte carlo bayesian methods for discrete regression models and categorical time series. In: Bernardo J., et al. (Ed). Bayesian Statistics 4 (1993), Oxford University Press, Oxford 577-586
8. Carlin B., Polsen N., and Stoffer D. A monte carlo approach to nonnormal and nonlinear state-space-modelling. Journal of the American Statistical Association 87 (1992) 493-500
9. Chen M., and Shao Q. Properties of prior and posterior distribution for multivariate categorical response data model. Journal of Multivariate Analysis 71 (1999) 277-296
10. Chib S., and Greenberg E. Analysis of multivariate probit models. Biometrika 85 2 (1998) 347-361
11. Cowles M., and Carlin B. Markov chain monte carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association 91 (1996) 883-904
12. Cox D., and Reid N. A note on pseudolikelihood constructed from marginal densities. Biometrika 91 (2004) 729-737
13. Daganzo C. Multinomial probit. The theory and its application to demand forecasting. Economic Theory, Econometrics, and Mathematical Economics (1979), Academic Press, New York etc A Subsidiary of Harcourt Brace Jovanovich, Publishers. XIV, 222 p
14. A.R. De Leon, Pairwise likelihood approach to grouped continuous model and its extension, Statistics and Probability Letters (2005)
15. Diggle P.J., Heagerty P.J., Liang K., and Zeger S.L. Analysis of longitudinal data. Oxford Statistical Science Series. 2nd ed. vol. 25 (2003), Oxford University Press, Oxford xv, 379 p
16. Epron D., Nouvellon Y., Roupsard O., Mouvondy W., Mabiala A., Saint-André L., Joffre R., Jourdan C., Bonnefond J.-M., Berbigier P., and Hamel O. Spatial and temporal variation of soil respiration in a eucalyptus plantation in congo. Forest Ecology and Management 202 (2006) 149-160
17. Fahrmeir L. Posterior mode estimation by extended kalman filtering for multivariate dynamic generalized linear models. Journal of the American Statistical Association 87 (1992) 501-509
18. L. Fahrmeir, R. Künstler, Penalized likelihood smoothing in robust state space models. Metrika, 173-191
19. Gelfand A.E., and Smith A.F. Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85 410 (1990) 398-409
20. Gelman A., Carlin J.B., Stern H.S., and Rubin D.B. Bayesian data analysis. 2nd ed (2004), Chapman and Hall/CRC, Boca Raton, FL xxv, 668 p
21. Geweke J. Efficient simulation from the multivariate normal and student-t distributions subject to linear constraints. In: Keramidas E.M. (Ed). Computing Science and Statistics: Proceedings of the Twenty Third Symposium on the Interface (1991) 571-578
22. Hastings W. Monte carlo sampling methods using markov chains and their applications. Biometrika 57 (1970) 97-109
23. P.J. Huber, Robust statistics, in: Wiley Series in Probability and Mathematical Statistics, John Wiley & Sons, New York etc, IX, 308 p
24. Joe H. Multivariate models and dependence concepts. Monographs on Statistics and Applied Probability vol. 73 (1997), Chapman and Hall, London xviii, 399 p
25. Kauermann G. Modeling longitudinal data with ordinal response by varying coefficients. Biometrics 56 3 (2000) 692-698
26. Kotz S., Balakrishnan N., and Johnson N.L. Continuous multivariate distributions. Vol. 1: Models and Applications. 2nd ed. (2000), Wiley, New York, NY xxii, 722 p
27. Lesaffre E., and Molenberghs G. Multivariate probit analysis: A neglected procedure in medical statistics. Statistics in Medicine 10 (1991) 1391-1403
28. Liu L., and Hedeker D. A mixed-effects regression model for longitudinal multivariate ordinal data. Biometrics 62 (2006) 261-268
29. R. Lord, Applications of item response theory to practical testing problems, 1980, Erlbaum, Hillside, NJ
30. McCulloch C.E., and Searle S.R. Generalized, linear and mixed models. Wiley Series in Probability and Statistics (2001) 358 p
31. Mortier F., Robin S., Lassalvy S., P. B.C., and Bar-Hen A. Prediction of euclidean distances with discrete and continuous outcomes. Journal of Multivariate Analysis 97 8 (2006) 1799-1814
32. O'Brien S.M., and Dunson D.B. Bayesian multivariate logistic regression. Biometrics 60 (2004) 739-746
33. Pardé J. Forest biomass. Forestry Abstract Review Article 41 8 (1980) 343-362
34. Parresol B. Assessing tree and stand biomass: A review with examples and critical comparisons. Forest Science 45 4 (1999)
35. Rekayaa R., Gianola D., and Shookb G. Longitudinal random effects models for genetic analysis of binary data with application to mastitis in dairy cattle. Genetics Selections Evolution 35 (2003) 457-468
36. Rivers D., and Vuong Q. Model selection tests for nonlinear dynamic models. The Econometrics Journal 5 (2002) 1-10
37. L. Saint-André, J.-P. Laclau, J.-P. Bouillet, P. Deleporte, A. Miabala, N. Ognouabi, H. Bailléres, Y. Nouvellon, 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, 2002, pp. 611-621
38. Saint-André L., Thongo A., Mabiala A., Mouvondy W., Jourdan C., Roupsard O., Deleporte P., Hamel O., and Nouvellon Y. Age related equations for above and below biomass of a eucalyptus hybrid in congo. Forest Ecology and Management 205 (2005) 199-214
39. Sicard C., Saint-André L., Gelhaye D., and Ranger J. Effect of initial fertilisation on biomass and nutrient content of Norway Spruce and Douglas-fir plantations at the same cite. Trees Structure and Function 20 2 (2006) 229-246
40. Ten Have T.R., and Morabia A. Mixed effects models with bivariate and univariate association parameters for longitudinal bivariate binary response data. Biometrics 55 1 (1999) 85-93
41. Zhang D. Generalized linear mixed models with varying coefficients for longitudinal data. Biometrics 60 1 (2004) 8-15