TAILIEUCHUNG - Báo cáo sinh học: "Use of linear mixed models for genetic evaluation of gestation length and birth weight allowing for heavy-tailed residual effects"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Use of linear mixed models for genetic evaluation of gestation length and birth weight allowing for heavy-tailed residual effects | Kizilkaya et al. Genetics Selection Evolution 2010 42 26 http http content 42 1 26 GSE Ge n et i cs Selection Evolution RESEARCH Open Access Use of linear mixed models for genetic evaluation of gestation length and birth weight allowing for heavy-tailed residual effects 1 2 4 Kadir Kizilkaya 1 Dorian J Garrick 1 Rohan L Fernando Burcu Mestav Mehmet A Yildiz Abstract Background The distribution of residual effects in linear mixed models in animal breeding applications is typically assumed normal which makes inferences vulnerable to outlier observations. In order to mute the impact of outliers one option is to fit models with residuals having a heavy-tailed distribution. Here a Student s-t model was considered for the distribution of the residuals with the degrees of freedom treated as unknown. Bayesian inference was used to investigate a bivariate Student s-t BSt model using Markov chain Monte Carlo methods in a simulation study and analysing field data for gestation length and birth weight permitted to study the practical implications of fitting heavy-tailed distributions for residuals in linear mixed models. Methods In the simulation study bivariate residuals were generated using Student s-t distribution with 4 or 12 degrees of freedom or a normal distribution. Sire models with bivariate Student s-t or normal residuals were fitted to each simulated dataset using a hierarchical Bayesian approach. For the field data consisting of gestation length and birth weight records on 7 883 Italian Piemontese cattle a sire-maternal grandsire model including fixed effects of sex-age of dam and uncorrelated random herd-year-season effects were fitted using a hierarchical Bayesian approach. Residuals were defined to follow bivariate normal or Student s-t distributions with unknown degrees of freedom. Results Posterior mean estimates of degrees of freedom parameters seemed to be accurate and unbiased in the simulation study. Estimates of sire and herd .

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