tailieunhanh - Báo cáo khoa hoc:" EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis | Genet. Sel. Evol. 32 2000 129-141 129 INRA EDP Sciences Original article EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis Jean-Louis FOULLEYa Florence JAFFREZICb Christele ROBERT-GRANIEa a Station de génétique quantitative et appliquée Institut national de la recherche agronomique 78352 Jouy-en-Josas Cedex France b Institute of Cell Animal and Population Biology The University of Edinburgh Edinburgh EH9 3JT UK Received 24 September 1999 accepted 30 November 1999 Abstract - This paper presents procedures for implementing the EM algorithm to compute REML estimates of variance covariance components in Gaussian mixed models for longitudinal data analysis. The class of models considered includes random coefficient factors stationary time processes and measurement errors. The EM algorithm allows separation of the computations pertaining to parameters involved in the random coefficient factors from those pertaining to the time processes and errors. The procedures are illustrated with Pothoff and Roy s data example on growth measurements taken on 11 girls and 16 boys at four ages. Several variants and extensions are discussed. EM algorithm REML mixed models random regression longitudinal data Résumé Estimation EM-REML des parametres de covariance en modèles mixtes gaussiens en vue de l analyse de données longitudinales. Cet article présente des procédés permettant de mettre en reuvre l algorithme EM en vue du calcul d estimations REML des composantes de variance covariance en modeles mixtes gaussiens d analyse de données longitudinales. La classe de modèles considérée concerne les coefficients aléatoires les processus temporels stationnaires et les erreurs de mesure. L algorithme EM permet de dissocier formellement les calculs relatifs aux paramètres des coefficients aléatoires de ceux impliqués dans les processus et la résiduelle. Ces methodes sont illustrees par un exemple provenant de Pothoff et Roy Correspondence and .