The Alpha-Beta-Skew-Logistic Distribution And Its Applications

TL;DR

This paper introduces the alpha-beta-skew-logistic distribution, explores its properties, and demonstrates its applicability through real-world data, comparing model fit with existing distributions using AIC, BIC, and likelihood ratio tests.

Contribution

It proposes a new skew logistic distribution and evaluates its properties and effectiveness in modeling real data, extending the family of skew distributions.

Findings

The distribution has flexible skewness and bimodality.

It outperforms some existing distributions in real data applications.

Likelihood ratio tests favor the proposed distribution over logistic models.

Abstract

In this paper, an alpha-beta-skew-logistic distribution is proposed following the same methodology as those of alpha-beta-skew-normal of Shafiei et al. (2016) and investigated some of its related distributional properties. Finally, the validity of our proposed distribution has tested by considering three real life applications and comparing the values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) with the values of some other related distributions. Likelihood ratio test is used for discriminating between logistic and the proposed distributions. Keywords: Skew Distributions, Alpha-Skew Distributions, Bimodal Distributions