TAILIEUCHUNG - Báo cáo sinh học: " Bayes factor between Student t and Gaussian mixed models within an animal breeding context"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: Bayes factor between Student t and Gaussian mixed models within an animal breeding context | Genet. Sel. Evol. 40 2008 395-413 INRA EDP Sciences 2008 DOI gse 2008007 Available online at Original article Bayes factor between Student t and Gaussian mixed models within an animal breeding context Joaquim Casellas1 Noelia IbANez-Escriche1 LuisAlberto García-Cortés LuĩsVarona1 1Gen ỉtica i Millora Animal IRTA-Lleida 25198 Lleida Spain 2Departamento de Mejora Genetica Animal SGIT-INIA Carretera de la Coruna km. 7 28040 Madrid Spain Received 2 April 2007 accepted 19 December 2007 Abstract - The implementation of Student t mixed models in animal breeding has been suggested as a useful statistical tool to effectively mute the impact of preferential treatment or other sources of outliers in field data. Nevertheless these additional sources of variation are undeclared and we do not know whether a Student t mixed model is required or if a standard and less parameterized Gaussian mixed model would be sufficient to serve the intended purpose. Within this context our aim was to develop the Bayes factor between two nested models that only differed in a bounded variable in order to easily compare a Student t and a Gaussian mixed model. ít is important to highlight that the Student t density converges to a Gaussian process when degrees of freedom tend to infinity. The two models can then be viewed as nested models that differ in terms of degrees of freedom. The Bayes factor can be easily calculated from the output of a Markov chain Monte Carlo sampling of the complex model Student t mixed model . The performance of this Bayes factor was tested under simulation and on a real dataset using the deviation information criterion DíC as the standard reference criterion. The two statistical tools showed similar trends along the parameter space although the Bayes factor appeared to be the more conservative. There was considerable evidence favoring the Student t mixed model for data sets simulated under Student t processes with limited degrees of freedom .

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