tailieunhanh - Báo cáo sinh học: "Heterogeneous variances in Gaussian linear mixed models"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Heterogeneous variances in Gaussian linear mixed models | Genet Sei Evol 1995 27 211-228 Elsevier INRA 211 Original article Heterogeneous variances in Gaussian linear mixed models JL Foulley1 RL Quaas2 Institut national de la recherche agronomique station de génétique quantitative et appliquée 78352 Jouy-en-Josas France 2 Department of Animal Science Cornell University Ithaca NY If853 USA Received 28 February 1994 accepted 29 November 1994 Summary - This paper reviews some problems encountered in estimating heterogeneous variances in Gaussian linear mixed models. The one-way and multiple classification cases are considered. EM-REML algorithms and Bayesian procedures are derived. A structural mixed linear model on log-variance components is also presented which allows identification of meaningful sources of variation of heterogeneous residual and genetic components of variance and assessment of their magnitude and mode of action. heteroskedasticity mixed linear model restricted maximum likelihood Bayesian statistics Resume Variances hétérogènes en modèle linéaire mixte gaussien. Get article fait le point sur un certain nombre de problèmes qui surviennent tors de I estimation de variances hétérogènes dans des modèles linéaires mixtes gaussiens. On considère le cos d un OU plusieurs facteurs d hétéroscédasticité. On développe des algorithmes EM-REML et bayésiens. On propose également un modèle linéaire mixte structurel des logarithmes des variances qui permet de mettre en evidence des sources significatives de variation des variances residuelies et génétiques et d apprehender leur importance et leur mode d action. hétéroscédasticité modèle linéaire mixte maximum de vraisemblance résiduelle statistique bayésienne INTRODUCTION Genetic evaluation procedures in animal breeding rely mainly on best linear unbiased prediction BLUP and restricted maximum likelihood REML estimation of parameters of Gaussian linear mixed models Henderson 1984 . Although BLUP can accommodate heterogeneous variances Gianola 1986 most applications of 212