TAILIEUCHUNG - Báo cáo sinh học: "Phenotypic plasticity of body pigmentation in Drosophila: ECM approaches to heteroskedastic mixed models with constant variance ratios"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh Original cung cấp cho các bạn kiến thức về sinh học đề tài:Phenotypic plasticity of body pigmentation in Drosophila: ECM approaches to heteroskedastic mixed models with constant variance ratios. | 297 Genet Sei Evol 1997 29 297-318 Elsevier Inra Original article ECM approaches to heteroskedastic mixed models with constant variance ratios JL Foulley Station de génétique quantitative et appltquée Institut national de la recherche agronomique 78352 Jouy-en-Josas cedex France Received 6 February 1997 accepted 28 May 1997 Summary - This paper presents techniques of parameter estimation in heteroskedastic mixed models having constant variance ratios and heterogeneous log residual variances that are described by a linear model. Estimation of dispersion parameters is by standard ML and residual REML maximum likelihood. Estimating equations are derived using the expectation-conditional maximization ECM algorithm and simplified versions of it gradient ECM . Direct and indirect approaches are proposed with the latter allowing hypothesis testing about the variance ratios. The analysis of a small example is outlined to illustrate the theory. heteroskedasticity mixed model maximum likelihood EM algorithm Resume - Approches ECM des modèles mixtes hétéroscédastiques à rapports de variances constants. Cet article présente des techniques d estimation des paramètres intervenant dans des modèles mixtes ayant des rapports de variance constants et des variances résiduelles décrites par un modèle linéaire de leurs logarithmes. Les paramètres de dispersion sont estimés par le maximum de vraisemblance classique ML et restreint REML . Les equations à résoudre pour obtenir ces estimations sont établies à partir de I algorithme d esperance-maximisation conditionnelle ECM et d une version simplifiee dite du gradient ECM. Des approches directe et indirecte sont proposées cette demière conduisant à un test d hypothese sur le rapport de variances. La théorie est illustrée par I analyse numérique d un petit exemple. hétéroscédasticité modèle mixte maximum de vraisemblance algorithme EM INTRODUCTION Heteroskedasticity has recently generated much interest in quantitative genetics and animal .

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