tailieunhanh - Báo cáo sinh học: "Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution | Genet. Sel. Evol. 36 2004 49-64 INRA EDP Sciences 2004 DOI gse 2003050 49 Original article Full conjugate analysis of normal multiple traits with missing records using a generalized inverted Wishart distribution Rodolfo Juan Carlos Cantet1 b Ana Nelida BiRCHMEiERa Juan Pedro STEiBELa a Departamento de Production Animal Universidad de Buenos Aires Avenida San Martin 4453 1417 Buenos Aires Argentina b Consejo Nacional de Investigaciones Cientiflcas y Tecnicas CONICET Argentina Received 15 January 2003 accepted 7 August 2003 Abstract - A Markov chain Monte Carlo MCMC algorithm to sample an exchangeable covariance matrix such as the one of the error terms R0 in a multiple trait animal model with missing records under normal-inverted Wishart priors is presented. The algorithm FCG is based on a conjugate form of the inverted Wishart density that avoids sampling the missing error terms. Normal prior densities are assumed for the fixed effects and breeding values whereas the covariance matrices are assumed to follow inverted Wishart distributions. The inverted Wishart prior for the environmental covariance matrix is a product density of all patterns of missing data. The resulting MCMC scheme eliminates the correlation between the sampled missing residuals and the sampled R0 which in turn has the effect of decreasing the total amount of samples needed to reach convergence. The use of the FCG algorithm in a multiple trait data set with an extreme pattern of missing records produced a dramatic reduction in the size of the autocorrelations among samples for all lags from 1 to 50 and this increased the effective sample size from to 7 times and reduced the number of samples needed to attain convergence when compared with the data augmentation algorithm. FCG algorithm multiple traits missing data conjugate priors normal-inverted Wishart 1. INTRODUCTION Most data sets used to estimate genetic and environmental covariance components from multiple trait animal models .

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