Tham khảo luận văn - đề án 'báo cáo sinh học: " assessment of a poisson animal model for embryo yield in a simulated multiple ovulation-embryo transfer scheme"', luận văn - báo cáo phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 263 Genet Sei Evol 1994 26 263-290 Elsevier INRA Original article Assessment of a Poisson animal model for embryo yield in a simulated multiple ovulation-embryo transfer scheme RJ Tempelman1 D Gianola2 1 University of Wisconsin-Madison Department of Dairy Science 2 University of Wisconsin-Madison Department of Meat and Animal Science 1675 Observatory Drive Madison WI 53706 USA Received 16 March 1993 accepted 10 January 1994 Summary - Estimation and prediction techniques for Poisson and linear animal models were compared in a simulation study where observations were modelled as embryo yields having a Poisson residual distribution. In a one-way model fixed mean plus random animal effect with genetic variance T2 equal to or on a log linear scale Poisson marginal maximum likelihood MML gave estimates of ƠU with smaller empirical bias and mean squared error MSE than restricted maximum likelihood REML analyses of raw and log-transformed data. Likewise estimates of residual variance the average Poisson parameter were poorer when the estimation was by REML. These results were anticipated as there is no appropriate variance decomposition independent of location parameters in che linear model. Predictions of random effects obtained from the mode of the joint posterior distribution of fixed and random effects under the Poisson mixed-model tended to have smaller empirical bias and MSE than best linear unbiased prediction BLUP . Although the latter method does not take into account nonlinearity and does not make use of the assumption that the residual distribution was Poisson predictions were essentially unbiased. After log transformation of the records however BLUP led to unsatisfactory predictions. When embryo yields of zero were ignored in the analysis Poisson animal models accounting for truncation outperformed REML and BLUP. A mixed-model simulation involving one fixed factor 15 levels and 2 random factors for 4 sets of variance components was also carried out
