Original article Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: variance components in a mixed linear model using Gibbs sampling CS Marginal inferences about Wang* JJ Rutledge D Gianola University of Wisconsin-Madison, Department of Meat and Animal Science, Madison, WI 53706-1284, USA (Received 9 March 1992; accepted 7 October 1992) Summary - Arguing from a Bayesian viewpoint, Gianola and Foulley (1990) derived a new method for estimation of variance components in a mixed linear model: variance estimation from integrated likelihoods (VEIL). Inference is based on the marginal posterior distribution of each of the variance components. Exact analysis requires numerical integration. In this. | Genet Sei Evol 1993 25 41-62 Elsevier INRA 41 Original article Marginal inferences about variance components in a mixed linear model using Gibbs sampling cs Wang J J Rutledge D Gianola University of Wisconsin-Madison Department of Meat and Animal Science Madison WI 53706-1284 USA Received 9 March 1992 accepted 7 October 1992 Summary - Arguing from a Bayesian viewpoint Gianola and Foulley 1990 derived a new method for estimation of variance components in a mixed linear model variance estimation from integrated likelihoods VEIL . Inference is based on the marginal posterior distribution of each of the variance components. Exact analysis requires numerical integration. In this paper the Gibbs sampler a numerical procedure for generating marginal distributions from conditional distributions is employed to obtain marginal inferences about variance components in a general univariate mixed linear model. All needed conditional posterior distributions are derived. Examples based on simulated data sets containing varying amounts of information are presented for a one-way sire model. Estimates of the marginal densities of the variance components and of functions thereof are obtained and the corresponding distributions are plotted. Numerical results with a balanced sire model suggest that convergence to the marginal posterior distributions is achieved with a Gibbs sequence length of 20 and that Gibbs sample sizes ranging from 300 - 3 000 may be needed to appropriately characterize the marginal distributions. variance components linear models Bayesian methods marginalization Gibbs sampler Resume Inferences marginales sur des composantes de variance dans un modèle linéaire mixte à 1 aide de 1 échantillonnage de Gibbs. Partant d un point de vue bayésien Gianola et Foulley 1990 ont établi une nouvelle methode d estimation des composantes de variance dans un modèle linéaire mixte estimation de variance par les vraisemblances intégrées VEIL . L inference est basée sur la .
