<p>SD资源，学校穷，呵呵：）</p><p><br/>　　　　【正题名】:Bayesian copula selection <br/>　　　　　【作者】:David Huard; Guillaume évin; Anne-Catherine Favre <br/>　　　【作者单位】:Institut National de la Recherche Scientifique, Centre Eau, Terre & Environnement, Qué., Canada G1K 9A9; Institut National de la Recherche Scientifique, Centre Eau, Terre & Environnement, Qué., Canada G1K 9A9; Institut National de la Recherche Scientifique, Centre Eau, Terre & Environnement, Qué., Canada G1K 9A9 <br/>　　　　　【刊名】:Computational statistics & data analysis <br/>　　　　【年卷期】:vol.51 <br/>　　　　【出版年】:2006 <br/>　　　　　【ISSN】:0167-9473 <br/>　　　　　【期号】:no.2 <br/>　　　　　【页码】:p. 809-822 <br/>　　　　【总页数】:14 <br/>　　　　【分类号】:TP3 <br/>　　　　【关键词】:Copulas; Model selection; Bayes’ theorem; Goodness-of-fit test; Kendall's tau; Pseudo-likelihood <br/>　　　【正文语种】:eng <br/>　　　　　【文摘】:In recent years, the use of copulas has grown extremely fast and with it, the need for a simple and reliable method to choose the right copula family. Existing methods pose numerous difficulties and none is entirely satisfactory. We propose a Bayesian method to select the most probable copula family among a given set. The copula parameters are treated as nuisance variables, and hence do not have to be estimated. Furthermore, by a parameterization of the copula density in terms of Kendall's τ, the prior on the parameter is replaced by a prior on τ, conceptually more meaningful. The prior on τ, common to all families in the set of tested copulas, serves as a basis for their comparison. Using simulated data sets, we study the reliability of the method and observe the following: (1) the frequency of successful identification approaches 100% as the sample size increases, (2) for weakly correlated variables, larger samples are necessary for reliable identification. <br/> </p><p><br/></p>