A Flexible Extended Exponential Distribution With Its Statistical Features, Inference, and Real Application

Abstract

       This paper introduces a new version of the exponential distribution, offering a more flexible model for real-life data, namely unit half logistic geometric exponential (UHLGeE). The essential statistical functions and features of the two-parameter proposed distribution are discussed. Different techniques, maximum likelihood, ordinary least squares, weighted least squares, and Cramer-Von-Mises minimum distance, are employed to estimate the two unknown parameters. Consequently, extensive simulation experiments are conducted to evaluate the performance of all estimation methods. Lastly, the efficacy and adaptability of the new distribution are illustrated through an analysis of a real data set. Based on the outcomes of the empirical and real applications, it is recommended to adopt and employ UHLGeE in additional applications due to its features and adaptability.