An Alternative Discrete Skew Logistic Distribution

TL;DR

This paper introduces a new discrete skew logistic distribution derived from a continuous distribution, explores its properties, compares it with existing models, and demonstrates its application through simulations and real data analysis.

Contribution

It proposes a novel discrete skew logistic distribution using a discretization approach that preserves the survival function, and discusses parameter estimation methods.

Findings

The distribution's properties are thoroughly characterized.

Maximum likelihood and method of proportion are effective for parameter estimation.

Simulation studies validate the inferential techniques.

Abstract

In this paper, an alternative Discrete skew Logistic distribution is proposed, which is derived by using the general approach of discretizing a continuous distribution while retaining its survival function. The properties of the distribution are explored and it is compared to a discrete distribution defined on integers recently proposed in the literature. The estimation of its parameters are discussed, with particular focus on the maximum likelihood method and the method of proportion, which is particularly suitable for such a discrete model. A Monte Carlo simulation study is carried out to assess the statistical properties of these inferential techniques. Application of the proposed model to a real life data is given as well.