On the log-concavity of the Wright function

TL;DR

This paper studies the log-concavity of the Wright function in a probabilistic context, establishing conditions for log-concavity, unimodality, and their implications for generalized entropies and distributions.

Contribution

It provides new results on the log-concavity and unimodality of the Wright function, linking these properties to generalized entropy constructions and identifying critical parameters.

Findings

Log-concavity holds for certain parameter ranges, including classical Mittag-Leffler cases.

Probabilistic Wright functions are always unimodal.

Strong unimodality occurs under specific parameter conditions.

Abstract

We investigate the log-concavity on the half-line of the Wright function $\phi(-\alpha,\beta,-x),$ in the probabilistic setting $\alpha\in (0,1)$ and $\beta \ge 0.$ Applications are given to the construction of generalized entropies associated to the corresponding Mittag-Leffler function. A natural conjecture for the equivalence between the log-concavity of the Wright function and the existence of such generalized entropies is formulated. The problem is solved for $\beta\geq\alpha$ and in the classical case $\beta = 1-\alpha$ of the Mittag-Leffler distribution, which exhibits a certain critical parameter $\alpha_*= 0.771667...$ defined implicitly on the Gamma function and characterizing the log-concavity. We also prove that the probabilistic Wright functions are always unimodal, and that they are multiplicatively strongly unimodal if and only if $\beta\geq\alpha$ or $\alpha\le 1/2$ and $\beta = 0.$