Profiled deviance for the multivariate linear mixed-effects model fitting

Eric Adjakossa (1, 2) , Grégory Nuel (1)

1 LPMA - Laboratoire de Probabilités et Modèles Aléatoires
2 UAC - Université d’Abomey-Calavi = University of Abomey Calavi

This paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the marginal residual terms are assumed uncorrelated and homoscedastic with possibly different standard deviations. The random effects covariance matrix is Cholesky factorized to directly estimate the variance components of these random effects. This strategy enables a consistent estimate of the random effects covariance matrix which, generally, has a poor estimate when it is grossly (or directly) estimated, using the estimating methods such as the EM algorithm. By using simulated data sets, we compare the estimates based on the present method with the EM algorithm-based estimates. We provide an illustration by using the real-life data concerning the study of the child's immune against malaria in Benin (West Africa).

Mots clés: multivariate linear mixed-effects model; consistent estimate; profiled deviance; REML criterion

Domaines: Méthodologie [stat.ME]

Eric Adjakossa, Grégory Nuel. Profiled deviance for the multivariate linear mixed-effects model fitting. 2017. ⟨hal-01494186v1⟩