کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
418253 | 681626 | 2007 | 18 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Sampling algorithms for generating joint uniform distributions using the vine-copula method Sampling algorithms for generating joint uniform distributions using the vine-copula method](/preview/png/418253.png)
An n-n-dimensional joint uniform distribution is defined as a distribution whose one-dimensional marginals are uniform on some interval I . This interval is taken to be [0,1] or, when more convenient [-12,12]. The specification of joint uniform distributions in a way which captures intuitive dependence structures and also enables sampling routines is considered. The question whether every n-dimensional correlation matrix can be realized by a joint uniform distribution remains open. It is known, however, that the rank correlation matrices realized by the joint normal family are sparse in the set of correlation matrices. A joint uniform distribution is obtained by specifying conditional rank correlations on a regular vine and a copula is chosen to realize the conditional bivariate distributions corresponding to the edges of the vine. In this way a distribution is sampled which corresponds exactly to the specification. The relation between conditional rank correlations on a vine and correlation matrix of corresponding distribution is complex, and depends on the copula used. Some results for the elliptical copulae are given.
Journal: Computational Statistics & Data Analysis - Volume 51, Issue 6, 1 March 2007, Pages 2889–2906