TAILIEUCHUNG - Báo cáo sinh học: "On biased inferences about variance components in the binary threshold model"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: On biased inferences about variance components in the binary threshold model | Genet Sei Evol 1997 29 145-160 Elsevier INRA 145 Original article On biased inferences about variance components in the binary threshold model c Moreno1 D Sorensen2 LA García-Cortés L Varona1 J Altarriba1 1 Unidad de Genética Cuantitativa y Mejora Animal Facultad de Veterinaria Universidad de Zaragoza Miguel Servet 177 50013 Zaragoza Spain 2 Danish Institute of Animal Science Research Center Foulum PO Box 39 DK-8830 Tjele Denmark Received 2 September 1996 accepted 6 February 1997 Summary - A simulation study was conducted to study frequentist properties of three estimators of the variance component in a mixed effect binary threshold model. The three estimators were the mode of a normal approximation to the marginal posterior distribution of the component which is denoted in the literature as marginal maximum likelihood MML the mean of the marginal posterior distribution of the component using the Gibbs sampler to perform the marginalisations GSR and third the mode of the joint posterior distributions of location and the variance parameter used in conjunction with the iterative bootstrap bias correction MJP-IBC . The latter was recently proposed in the literature as a method to obtain nearly unbiased estimators. The results of this study confirm that MML can yield biased inferences about the variance component and that the sign of the bias depends on the amount of information associated with either fixed effects or with random effects. GSR can produce positively biased inferences when the amount of data per fixed effect is small. When fixed effects are poorly estimated the bias persists despite the fact that posterior distributions are guaranteed to be proper and that the amount of information about the variance component is large. In this case the marginal posterior distribution of the variance component is highly peaked and symmetric but it shows a shift towards the right with respect to the true simulated value. This bias can be reduced by assigning a Gaussian .

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