On the strong Kotz approximation of Dirichlet random vectors

Details

Serval ID
serval:BIB_614C91BB553A
Type
Article: article from journal or magazin.
Collection
Publications
Title
On the strong Kotz approximation of Dirichlet random vectors
Journal
Statistics
Author(s)
Hashorva E., Kotz S.
ISSN
0233-1888
Publication state
Published
Issued date
2009
Peer-reviewed
Oui
Volume
43
Number
4
Pages
393-408
Language
english
Abstract
Let (X1, X2) be a bivariate Lp-norm generalized symmetrized Dirichlet (LpGSD) random vector with parameters 1,2. If p=1=2=2, then (X1, X2) is a spherical random vector. The estimation of the conditional distribution of Zu*:=X2 | X1u for u large is of some interest in statistical applications. When (X1, X2) is a spherical random vector with associated random radius in the Gumbel max-domain of attraction, the distribution of Zu* can be approximated by a Gaussian distribution. Surprisingly, the same Gaussian approximation holds also for Zu:=X2| X1=u. In this paper, we are interested in conditional limit results in terms of convergence of the density functions considering a d-dimensional LpGSD random vector. Stating our results for the bivariate setup, we show that the density function of Zu* and Zu can be approximated by the density function of a Kotz type I LpGSD distribution, provided that the associated random radius has distribution function in the Gumbel max-domain of attraction. Further, we present two applications concerning the asymptotic behaviour of concomitants of order statistics of bivariate Dirichlet samples and the estimation of the conditional quantile function.
Keywords
Lp-norm generalized symmetrized Dirichlet distribution, Conditional limit theorem, Kotz type I LpGSD distribution, Gumbel max-domain of attraction, Concomitants of order statistics, Estimation of conditional quantile function
Web of science
Create date
03/09/2010 11:14
Last modification date
20/08/2019 15:18
Usage data