The Generalized-Alpha-Beta-Skew-Normal Distribution: Properties and Applications

TL;DR

This paper introduces a generalized alpha beta skew-normal distribution, explores its properties and extensions, and evaluates its suitability for modeling data using AIC, BIC, and likelihood ratio tests.

Contribution

It presents a new generalized distribution extending the alpha beta skew-normal, along with its properties and applications in model selection.

Findings

The proposed distribution fits data better based on AIC and BIC.

Likelihood ratio tests effectively discriminate between nested models.

Extensions of the distribution enhance modeling flexibility.

Abstract

In this paper we have introduced a generalized version of alpha beta skew normal distribution in the same line of Sharafi et al. (2017) and investigated some of its basic properties. The extensions of the proposed distribution have also been studied. The appropriateness of the proposed distribution has been tested by comparing the values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) with the values of some other known related distributions for better model selection. Likelihood ratio test has been used for discriminating between nested models.