AbstractThis paper is concerned with the distribution of a multivariate scale mixture variate X=(X1,…,Xp)′ with Xi=SiZi, where Z1,…,Zp are i.i.d. random variables, Si>0(i=1,…,p), and {S1,…,Sp} is independent of {Z1,…,Zp}. First we obtain L1-norm error bounds for an asymptotic expansion of the density function of X in the multivariate case as well as in the univariate case. Then the results are applied in obtaining error bounds for asymptotic expansions of the null distribution of Hotelling's generalized T02-statistic. The special features of our results are that our error bounds are given in explicit and computable forms. Further, their orders are the same as ones of the usual order estimates, and hence the paper provides a new proof for va...
AbstractWe study multivariate approximation with the error measured in L∞ and weighted L2 norms. We ...
In this paper, we develop local expansions for the ratio of the centered matrix-variate $T$ density ...
The problem of classifying an observation X into one of k multivariate normal distributions is consi...
AbstractThis paper is concerned with the distribution of a multivariate scale mixture variate X=(X1,...
AbstractLet X = σZ be a scale mixture of a random variable with the scale factor σ. In this paper we...
AbstractLet Z be a random vector following the p-variate normal distribution N(0, Ip), and let S be ...
AbstractLet Y be an absolutely continuous random variable and W a nonnegative variable independent o...
AbstractLet Λ=|Se|/|Se+Sh|, where Sh and Se are independently distributed as Wishart distributions W...
ABSTRACT: Some standard test statistics in multivariate analysis have a common na-ture, that is, the...
AbstractThe kernel estimator of a multivariate probability density function is studied. An asymptoti...
We consider phase–type scale mixture distributions which correspond to distri-butions of random vari...
AbstractSuppose that U=(U1,…,Ud) has a Uniform([0,1]d) distribution, that Y=(Y1,…,Yd) has the distri...
Zhang derives approximations for the distribution of a mixture of chi-squared distributions. The two...
AbstractTheoretical accuracies are studied for asymtotic approximations of the expected probabilitie...
Abstract: Problem statement: Hotelling’s T2 statistic has been well documented in the existing liter...
AbstractWe study multivariate approximation with the error measured in L∞ and weighted L2 norms. We ...
In this paper, we develop local expansions for the ratio of the centered matrix-variate $T$ density ...
The problem of classifying an observation X into one of k multivariate normal distributions is consi...
AbstractThis paper is concerned with the distribution of a multivariate scale mixture variate X=(X1,...
AbstractLet X = σZ be a scale mixture of a random variable with the scale factor σ. In this paper we...
AbstractLet Z be a random vector following the p-variate normal distribution N(0, Ip), and let S be ...
AbstractLet Y be an absolutely continuous random variable and W a nonnegative variable independent o...
AbstractLet Λ=|Se|/|Se+Sh|, where Sh and Se are independently distributed as Wishart distributions W...
ABSTRACT: Some standard test statistics in multivariate analysis have a common na-ture, that is, the...
AbstractThe kernel estimator of a multivariate probability density function is studied. An asymptoti...
We consider phase–type scale mixture distributions which correspond to distri-butions of random vari...
AbstractSuppose that U=(U1,…,Ud) has a Uniform([0,1]d) distribution, that Y=(Y1,…,Yd) has the distri...
Zhang derives approximations for the distribution of a mixture of chi-squared distributions. The two...
AbstractTheoretical accuracies are studied for asymtotic approximations of the expected probabilitie...
Abstract: Problem statement: Hotelling’s T2 statistic has been well documented in the existing liter...
AbstractWe study multivariate approximation with the error measured in L∞ and weighted L2 norms. We ...
In this paper, we develop local expansions for the ratio of the centered matrix-variate $T$ density ...
The problem of classifying an observation X into one of k multivariate normal distributions is consi...