Showing papers on "Multivariate gamma function published in 2001"
TL;DR: In this paper, a new probability density function (P.D.F) with r-confluent hypergeometric function is considered, and several special cases for inverse gaussian, Weibull and gamma distributions are given.
Abstract: In the present work we consider a new probability density function (P.D.F) with r-confluent hypergeometric function. Special cases are considered, which include gamma, Weibull and inverse gausssian distributions. In order to study certain statistical properties, we introduce a new generalized gamma function together with its incomplete forms. We derive the basic functions associated with this P.D.F.: the mean, moments, expected values, moment generating function, hazard rate function, and mean residue life function. Several special cases for inverse gaussian, Weibull and gamma distributions are given. In the graphical representations of P.D.F. and hazard rate function, the role of shape and scale parameters is reflected.
19 citations
TL;DR: In this paper, the p-adic q-L-function at positive integers can be expressed in terms of the q-extension of p -adic log multiple gamma functions, and it is shown that q-integrals can be defined in n-variables.
Abstract: By using multiple p-adic q-integrals, we define the p-adic q-L-function in n-variables and the q-extension of p-adic log multiple gamma functions. From these definitions, we show that the values of the p-adic q-L-function at positive integers can be expressed in terms of the q-extension of p-adic log multiple gamma functions.
8 citations
TL;DR: In this article, the authors considered the Bayesian analysis of the multivariate normal distribution under a new and bounded loss function, based on a reflection of the multiivariate normal density function.
Abstract: This paper considers the Bayesian analysis of the multivariate normal distribution under a new and bounded loss function, based on a reflection of the multivariate normal density function. The Bayes estimators of the mean vector can be derived for an arbitrary prior distribution of [d]. When the covariance matrix has an inverted Wishart prior density, a Bayes estimator of[d] is obtained under a bounded loss function, based on the entropy loss. Finally the admissibility of all linear estimators c[d]+ d for the mean vector is considered
TL;DR: In this paper, a matrix variate generalization of multivariate Kummer-Beta and multivariate KG families of distributions has been proposed and studied by Ng and Kotz.
Abstract: The multivariate Kummer-Beta and multivariate Kummer-Gamma
families of distributions have been proposed and studied recently
by Ng and Kotz. These distributions are extensions of Kummer-Beta
and Kummer-Gamma distributions. In this article we propose and
study matrix variate generalizations of multivariate Kummer-Beta
and multivariate Kummer-Gamma families of distributions.
TL;DR: A mathematical model for a new distribution involving the generalized gamma function of Al-Musallam and Kalla is studied and some basic functions associated with the p.d.f., namely, the mean, moment generating function, hazard rate function and mean residue life function are derived.