A general class of continuous asymmetric distributions with positive support

TL;DR

This paper introduces a versatile class of asymmetric distributions with positive support, unifying several known models and providing mathematical properties useful for real-world data fitting and extreme value analysis.

Contribution

It proposes a new general distribution framework that encompasses many existing models and derives their mathematical properties for practical data modeling.

Findings

Unified framework for asymmetric distributions with positive support

Mathematical properties derived for the new distribution class

Validated performance on real-world datasets

Abstract

In order to better fit real-world datasets, studying asymmetric distribution is of great interest. In this work, we derive several mathematical properties of a general class of asymmetric distributions with positive support which shows up as a unified framework for Extreme Value Theory asymptotic results. The new model generalizes some well-known distribution models such as Generalized Gamma, Inverse Gamma, Weibull, Fr\'echet, Half-normal, Modified half-normal, Rayleigh, and Erlang. To highlight the applicability of our results, the performance of the analytical models is evaluated through real-life dataset modeling.