On the family of multivariate chi-square copulas

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• 作者：Jean-Franç ; ois Quessyp>ap>p> ; p>p>jean-francois.quessy@uqtr.ca" class="auth_mail" title="E-mail the corresponding authorp> ; Louis-Paul Rivestp>bp>p> ; p>p>louis-paul.rivest@mat.ulaval.ca" class="auth_mail" title="E-mail the corresponding authorp> ; Marie-Hé ; lè ; ne Toupinp>bp>p> ; p>p>marie-helene.toupin.1@ulaval.ca" class="auth_mail" title="E-mail the corresponding authorp>
• 关键词：62H99 ; 62H11
• 刊名：Journal of Multivariate Analysis
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：152
• 期：Complete
• 页码：40-60
• 全文大小：1612 K

文摘

This paper explores the theoretical properties and the practical usefulness of the general family of chi-square copulas that recently appeared in the literature. This class of dependence structures is very attractive, as it generalizes the Gaussian copula and allows for flexible modeling for high-dimensional random vectors. On one hand, expressions for the copula and the density in the bivariate and the multivariate case are derived and many theoretical properties are investigated, including expressions for popular measures of dependence, levels of asymmetry and constraints on the Kendall’s tau matrix. On the other hand, two applications of the chi-square copulas are developed, namely parameter estimation and spatial interpolation.