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báo cáo khoa học: "Restricted Maximum Likelihood to estimate variance components for mixed models with two random factors Karin MEYER"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Restricted Maximum Likelihood to estimate variance components for mixed models with two random factors Karin MEYER | Génét. Sél. Evol. 1987 19 1 49-68 Restricted Maximum Likelihood to estimate variance components for mixed models with two random factors Karin MEYER Institute of Animal Genetics University of Edinburgh West Mains Road Edinburgh EH9 3JN Scotland U.K. and Genetic Improvement of Livestock Department of Animal and Poultry Science University of Guelph Guelph Ontario NIG 2W1 Canada Summary A Restricted Maximum Likelihood procedure is described to estimate variance components for a univariate mixed model with two random factors. An EM-type algorithm is presented with a reparameterisation to speed up the rate of convergence. Computing strategies are outlined for models common to the analysis of animal breeding data allowing for both a nested and a crossclassified design of the 2 random factors. Two special cases are considered firstly the total number of levels of fixed effects is small compared to the number of levels of both random factors secondly one fixed effect with a large number of levels is to be fitted in addition to other fixed effects with few levels. A small numerical example is given to illustrate details. Key words Restricted Maximum Likelihood variance component estimation nested design full sib family structure. Résumé Estimation des composantes de la variance par le Maximum de Vraisemblance Restreint dans un modèle mixtè à deux facteurs aléatoữes Une methode d estimation des composantes de la variance par le Maximum de Vraisemblance Restreint est décrite dans le cas d un modèle mixte à une seule variable avec 2 facteurs aléatoires. Un algorithme de calcul du type E.M. est présenté avec une reparamétrisation pour accélérer la Vitesse de convergence. Des strategies de calcul sont abordées pour les modèles d analyse génétique les plus courants avec 2 facteurs aléatoires hiérarchìques OU croisés. Deux cas particu-liers sont décrits premièrement le nombre total de niveaux des effets fixes est faible comparati-vement à celui des facteurs aléatoires deuxièmement