A new family of generalized-G distributions with properties and applications

Abstract

We introduce and study a new generalized family of distributions herein referred to as the Marshall–Olkin Exponentiated Half Logistic-Generalized-G (MO-EHL-GG). A generalized distribution is a broader class of probability distributions that includes various specific distributions. It has parameters that allow for flexibility in modeling different types of data. By combining the Marshall–Olkin generator, the exponentiated half logistic generator and the generalized generator, the MO-EHL-GG family of distributions is developed. The primary objective behind introducing this new distribution is its enhanced flexibility and the ability of its hazard rate function to exhibit diverse shapes, making it valuable for statistical analysis and modeling purposes. Special cases of the new model are presented. Mathematical and statistical properties of the distribution are investigated. Estimates of the parameters are provided and simulation studies are conducted to examine the consistency of the model’s estimates. The significance of the new model is finally investigated through applications to real-life data sets. Three datasets were analyzed, demonstrating superior performance of our proposed distribution compared to competing models with the same number of parameters.

Keywords

• Generalized distribution
• Exponentiated half logistic distribution
• Statistical inference