TAILIEUCHUNG - Báo cáo sinh học: " Estimation of variance components of threshold characters by marginal posterior modes and means via "

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Estimation of variance components of threshold characters by marginal posterior modes and means via | Genet Sei Evol 1995 27 519-540 Elsevier INRA 519 Original article Estimation of variance components of threshold characters by marginal posterior modes and means via Gibbs sampling I Hoeschele B Tier Virginia Polytechnic Institute and State University Department of Dairy Science Blacksburg VA 24061-0315 USA Received 19 October 1994 accepted 24 August 1995 Summary - A Gibbs sampling scheme for Bayesian analysis of binary threshold data was derived. A simulation study was conducted to evaluate the accuracy of 3 variance component estimators deterministic approximate marginal maximum likelihood AMML Monte-Carlo marginal posterior mode MCMML and Monte-Carlo marginal posterior mean MCMPM . Several designs with different numbers of genetic groups herd-year-seasons HYS sires and progeny per sire were simulated. HYS were generated as fixed normally distributed or drawn from a proper uniform distribution. The downward bias of the AMML estimator for small family sizes 50 sires average of 40 progeny was eliminated with the MCMML estimator. For designs with many HYS incidence 50 sires and 40 progeny on average the marginal posterior distribution of heritability was non-normal MCMML and MCMPM significantly overestimated heritability under the sire mode while under the animal model the Gibbs sampler did not converge. For designs with 100 sires and 200 progeny per sire the marginal posterior distribution of heritability became more normal and the discrepancy among MCMML and MCMPM estimates vanished. Heritability estimates under the animal model were less accurate than those under the sire model. For the smaller designs the MCMML estimates were very close to the true value when using a normal prior for HYS effects irrespective of the true state of nature of the HYS effects. For extreme incidence small data sets and many HYS observations within an HYS will frequently fall into the same category of response. With flat priors for the HYS effects the posterior density is likely .

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