Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Inference about multiplicative heteroskedastic components of variance in a mixed linear Gaussian model with an application to beef cattle breeding | Genet Sei Evol 1993 25 3-30 Elsevier INRA 3 Original article Inference about multiplicative heteroskedastic components of variance in a mixed linear Gaussian model with an application to beef cattle breeding M San Cristobal1 JL Foulley1 E Manfredi2 1 INRA Station de Génétique Quantitative et Appliquée 78352 Jouy-en-Josas Cedex 2 INRA Station d Amelioration Génétique des Animaux BP 27 SI326 Castanet-Tolosan Cedex France Received 28 April 1992 accepted 23 September 1992 Summary - A statistical method for identifying meaningful sources of heterogeneity of residual and genetic variances in mixed linear Gaussian models is presented. The method is based on a structural linear model for log variances. Inference about dispersion parameters is based on the marginal likelihood after integrating out location parameters. A likelihood ratio test using the marginal likelihood is also proposed to test for hypotheses about sources of variation involved. A Bayesian extension of the estimation procedure of the dispersion parameters is presented which consists of determining the mode of their marginal posterior distribution using log inverted chi-square or Gaussian distributions as priors. Procedures presented in the paper are illustrated with the analysis of muscle development scores at weaning of 8 575 progeny of 142 sires in the Maine-Anjou breed. In this analysis heteroskedasticity is found both for the sire and residual components of variance. heteroskedasticity mixed linear model I Bayesian technique Resume - Inference sur une hétérogénéité multiplicative des composantes de la variance dans un modèle linéaire mixte gaussien application à la selection des bovins viande. Une méthode statistique est présentée capable d identifier les sources significatives d hétérogénéité de variances residuelies et génétiques dans un modèle linéaire mixte gaussien. La méthode est fondée sur un modèle structurel de decomposition du logarithme des variances. L inference concernant les paramètres de
