Moment properties of the multivariate Dirichlet distributions

被引:0
|
作者
Gupta, RD [1 ]
Richards, DS
机构
[1] Univ New Brunswick, St John, NB E2L 4L5, Canada
[2] Specola Vaticana, Vatican City, Vatican
[3] Univ Virginia, Dept Stat, Charlottesville, VA 22904 USA
关键词
characterizations; confluent hypergeometric function; multivariate beta distribution; multivariate Dirichlet distribution; Gaussian hypergeometric function; generalized power function; Laplace transform; multivariate gamma function; symmetric cone; Weyl fractional derivative; Wishart distribution; zonal polynomial;
D O I
10.1006/jmva.2001.2016
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Let X-1,...,X-n, be real, symmetric, m x m random matrices; denote by I,, the m x m identity matrix; and let a,..., a,, be fixed real numbers such that a(j) > (m - 1)/2, j = 1,...,n. Motivated by the results of J. G. Mauldon (Ann. Math. Statist. 30 (1959), 509-520) for the classical Dirichlet distributions, we consider the problem of characterizing the joint distribution of (X-1,...,X-n) subject to the condition that E\I-m - Sigma(j=1)(n)T(j)X(j)\(-(a1+...+an)) = Pi(j=1)(n)\I-m - T-j\(-aj) for all m x m symmetric matrices T-1,...,T-n in a neighborhood of the rn x m zero matrix. Assuming that the joint distribution of (X-1,...,X-n) is orthogonally invariant, we deduce the following results: each X-j is positive-definite, almost surely; X-1+...+X-n = I-m almost surely; the marginal distribution of the sum of any proper subset of X,..., X,, is a multivariate beta distribution; and the joint distribution of the determinants (\X-1\,....,\X-n\) is the same as the joint distribution of the determinants of a set of matrices having a multivariate Dirichlet distribution with parameter (a(1),...,a(n)). In particular, for n = 2 we obtain a new characterization of the multivariate beta distribution. (C) 2002 Elsevier Science (USA).
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页码:240 / 262
页数:23
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