Estimation of heterogeneous variances in nonlinear mixed models via the SAEM-MCMC algorithm with applications to growth curves in poultry
| Autor: | Mylene Duval, Christian Robert, Jean Louis Foulley |
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| Přispěvatelé: | Station d'Amélioration Génétique des Animaux (SAGA), Institut National de la Recherche Agronomique (INRA), Génétique Animale et Biologie Intégrative (GABI), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech |
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| Zdroj: | HAL Journal de la Société Française de Statistique Journal de la Société Française de Statistique, Société Française de Statistique et Société Mathématique de France, 2009, 150 (2), pp.65-83 |
| ISSN: | 1962-5197 2102-6238 |
| Popis: | The SAEM-MCMC algorithm is a powerful tool for computing maximum likelihood estimators in the wide class of nonlinear mixed effects models. We propose in this article an adaptation of this algorithm to the estimation of heterogeneous variances in such models. Two residual variance models are considered: a linear mixed model on the log-variance, with fixed and random effects, and a mean-variance relationship. As compared to other procedures implemented in R, SAS and Monolix, our algorithm provides more flexibility in modelling variance functions and reliability of the estimates. This algorithm was numerically validated in the case of a heteroskedastic linear mixed model by comparing its results with those of a standard EM algorithm applied to Pothoff and Roy’s data. Finally, an application to real data involving a selection experiment on growth in chickens is presented in which that algorithm was compared to results of SAS-Nlmixed, nlme, Monolix and WinBUGS softwares. |
| Databáze: | OpenAIRE |
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