Some informational inequalities involving generalized trigonometric functions and a new class of generalized moments

TL;DR

None

Contribution

None

Abstract

In this work, we define a family of probability densities involving the generalized trigonometric functions defined by Dr\'abek and Man\'asevich [1], which we name Generalized Trigonometric Densities. We show their relationship with the generalized stretched Gaussians and other types of laws such as logistic, hyperbolic secant, and raised cosine probability densities. We prove that, for a fixed generalized Fisher information, this family of densities is of minimal R\'enyi entropy. Moreover, we introduce generalized moments via the mean of the power of a deformed cumulative distribution. The latter is defined as a cumulative of the power of the probability density function, this second parameter tuning the tail weight of the deformed cumulative distribution. These generalized moments coincide with the usual moments of a deformed probability distribution with a regularized tail. We show that, for any bounded probability density, there exists a critical value for this second parameter below which the whole subfamily of generalized moments is finite for any positive value of the first parameter (power of the moment). In addition, we show that such generalized moments satisfy remarkable properties like order relation w.r.t. the first parameter, or adequate scaling behavior. Then we highlight that, if we constrain such a generalized moment, both the R\'enyi entropy and generalized Fisher information achieve respectively their maximum and minimum for the generalized trigonometric densities. Finally, we emphasis that GTDs and cumulative moments can be used to formally characterize heavy-tailed distributions, including the whole family of stretched Gaussian densities.