Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

In this article, we present a concise review of developments on various continuous multivariate distributions. We first present some basic definitions and notations. Then, we present several important continuous multivariate distributions and list their significant properties and characteristics.


Keywords:

generating function;
moments;
conditional distribution;
truncated distribution;
regression;
bivariate normal;
multivariate normal;
multivariate exponential;
multivariate gamma;
dirichlet;
inverted dirichlet;
liouville;
multivariate logistic;
multivariate pareto;
multivariate extreme value;
multivariate t;
wishart translated systems;
multivariate exponential families

/pdf/continuous-multivariate-distributions-1gnnj8yohf.pdf

Continuous Multivariate Distributions

Consider the function defined for $\alpha _{1},\ \alpha _{2} \in \mathbb{R}$ (α 1 2 +α 2 2 ≠ 0) and $\beta _{1},\beta _{2} \in \mathbb{C}$ by the series 

$$\displaystyle{ E_{\alpha _{1},\beta _{1};\alpha _{2},\beta _{2}}(z) \equiv \sum _{k=0}^{\infty } \frac{z^{k}} {\varGamma (\alpha _{1}k +\beta _{1})\varGamma (\alpha _{2}k +\beta _{2})}\ \ (z \in \mathbb{C}). }$$

(6.1.1)

Such a function with positive α 1 > 0, α 2 > 0 and real $\beta _{1},\beta _{2} \in \mathbb{R}$ was introduced by Dzherbashian [Dzh60].

Multi-index Mittag-Leffler Functions

Motivated essentially by the success of the applications of the Mittag-Leffler functions in many areas
of science and engineering, the authors present, in a unified manner, a detailed account or rather a brief
survey of the Mittag-Leffler function, generalized Mittag-Leffler functions, Mittag-Leffler type functions,
and their interesting and useful properties. Applications of G. M. Mittag-Leffler functions in certain areas of
physical and applied sciences are also demonstrated. During the last two decades this function has come
into prominence after about nine decades of its discovery by a Swedish Mathematician Mittag-Leffler,
due to the vast potential of its applications in solving the problems of physical, biological, engineering, and
earth sciences, and so forth. In this survey paper, nearly all types of Mittag-Leffler type functions existing in the
literature are presented. An attempt is made to present nearly an exhaustive list of references concerning
the Mittag-Leffler functions to make the reader familiar with the present trend of research in Mittag-Leffler
type functions and their applications.

https://downloads.hindawi.com/journals/jam/2011/298628.pdf

Mittag-Leffler Functions and Their Applications

Motivated essentially by the success of the applications of the Mittag-Leffler functions in many areas of science and engineering, the authors present in a unified manner, a detailed account or rather a brief survey of the Mittag- Leffler function, generalized Mittag-Leffler functions, Mittag-Leffler type functions, and their interesting and useful properties. Applications of Mittag-Leffler functions in certain areas of physical and applied sciences are also demonstrated. During the last two decades this function has come into prominence after about nine decades of its discovery by a Swedish mathematician G.M. Mittag-Leffler, due its vast potential of its applications in solving the problems of physical, biological, engineering and earth sciences etc. In this survey paper, nearly all types of Mittag-Leffler type functions existing in the literature are presented. An attempt is made to present nearly an exhaustive list of references concerning the Mittag-Leffler functions to make the reader familiar with the present trend of research in Mittag-Leffler type functions and their applications.

Discrete Mittag-Leffler distributions

Marshall–Olkin semi-Burr and Marshall–Olkin Burr distributions are introduced and studied. Their various characteristics in reliability analysis are derived. Applications in time series analysis are discussed.

On a generalization to Marshall–Olkin scheme and its application to Burr type XII distribution

The first-order autoregressive semi-Mittag-Leffler (SMLAR(1)) process is introduced and its properties are studied. As an illustration, we discuss the special case of the first-order autoregressive Mittag-Leffler (MLAR(I)) process. EAR(1) PROCESS; SEMI-MITTAG-LEFFLER DISTRIBUTION

The first-order autoregressive mittag-leffler process

A bivariate INAR(1) time series model with geometric marginals

Characterizations of bivariate geometric distribution using univariate and bivariate geometric compounding are obtained. Autoregressive models with marginals as bivariate geometric distribution are developed. Various bivariate geometric distributions analogous to important bivariate exponential distributions like, Marshall-Olkin’s bivariate exponential, Downton’s bivariate exponential and Hawkes’ bivariate exponential are presented.

/pdf/on-bivariate-geometric-distribution-29vsrpqpd8.pdf

K. Jayakumar

Papers

Discrete Mittag-Leffler distributions

On a generalization to Marshall–Olkin scheme and its application to Burr type XII distribution

The first-order autoregressive mittag-leffler process

A bivariate INAR(1) time series model with geometric marginals

On bivariate geometric distribution