Mittag-Leffler distribution

Abstract: Let ${\bf L}$ be the unit exponential random variable and ${\bf Z}_\alpha$ the standard positive $\alpha$-stable random variable. We prove that $\{(1-\alpha)\alpha^{\gamma_\alpha} {\bf Z}_\alpha^{-\gamma_\alpha}, 0< \alpha <1\}$ is decreasing for the optimal stochastic order and that $\{(1-\alpha){\bf Z}_\alpha^{ \gamma_\alpha}, 0< \alpha < 1\}$ is increasing for the convex order, with $\gamma_\alpha = \alpha/(1-\alpha).$ We also show that $\{\Gamma(1+\alpha) {\bf Z}_\alpha^{-\alpha}, 1/2\le \alpha \le 1\}$ is decreasing for the convex order, that ${\bf Z}_\alpha^{ \alpha}\,\prec_{st}\, \Gamma(1-\alpha) {\bf L}$ and that $\Gamma(1+\alpha){\bf Z}_\alpha^{-\alpha} \,\prec_{cx}\,{\bf L}.$ This allows to compare ${\bf Z}_\alpha$ with the two extremal Frechet distributions corresponding to the behaviour of its density at zero and at infinity. We also discuss the applications of these bounds to the strange behaviour of the median of ${\bf Z}_\alpha$ and ${\bf Z}_\alpha^{-\alpha}$ and to some uniform estimates on the classical Mittag-Leffler function. Along the way, we obtain a canonical factorization of ${\bf Z}_\alpha$ for $\alpha$ rational in terms of Beta random variables. The latter extends to the one-sided branches of real strictly stable densities.

Abstract: This paper is a continuation of cite{KP06}, where we discussed the origins and inter-relations of major types of skew Laplace distributions. Here, we review the properties of classical and geometric infinite divisibility as well as self-decomposability, which are crucial in extending univariate Laplace models to stochastic processes. General schemes based on these properties lead to several new non-Gaussian stationary autoregressive processes and continuous-time L'evy processes having potential use in stochastic modeling.

Abstract: The Mittag-Leffler stochastic process is an important tool in practical applications. In this paper, we first focus on some claims of Burr type-XII as a superstatistical stationary distribution (Sanchez, 2019). Then, we present the capabilities of the Mittag-Leffler distribution and introduce truncated Mittag-Leffler distributions. Finally, in the oil price analysis of time series real data, we show that the truncated Mittag-Leffler distribution performs better than other nominated distributions, especially the Burr distribution.