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Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Bayesian inference in the semiparametric log normal frailty model using Gibbs sampling | Genet. Sei. Evol. 30 1998 241-256 Inra Elsevier Paris 241 Original article Bayesian inference in the semiparametric log normal frailty model using Gibbs sampling Inge Riis Korsgaard Per Madsen Just Jensen Department of Animal Breeding and Genetics Research Centre Foulum Danish Institute of Agricultural Sciences P.O. Box 50 DK-8830 Tjele Denmark Received 16 October 1997 accepted 23 April 1998 Abstract - In this paper a full Bayesian analysis is carried out in a semiparametric log normal frailty model for survival data using Gibbs sampling. The full conditional posterior distributions describing the Gibbs sampler are either known distributions or shown to be log concave so that adaptive rejection sampling can be used. Using data augmentation marginal posterior distributions of breeding values of animals with and without records are obtained. As an example disease data on future AI-bulls from the Danish performance testing programme were analysed. The trait considered was time from entering test until first time a respiratory disease occurred . Bulls without a respiratory disease during the test and those tested without disease at date of analysing data had right censored records. The results showed that the hazard decreased with increasing age at entering test and with increasing degree of heterozygosity due to crossbreeding. Additive effects of gene importation had no influence. There was genetic variation in log frailty as well as variation due to herd of origin by period and year by season. Inra Elsevier Paris survival analysis semiparametric log normal frailty model Gibbs sampling animal model disease data on performance tested bulls Resume - Inference Bayésỉenne dans un modèle de survie semiparamétrique log-normal à partir de 1 échantillonnage de Gibbs. Une analyse complètement Bayésienne utilisant 1 échantillonnage de Gibbs a été effectuee dans un modèle de survie semiparamétrique log-normal. Les distributions conditionnelles a posteriori raises à profit par 1
