An Analysis of Generalized Exponential Uniform Distribution

Abstract

A two-parameter distribution under “Generalized Exponential Uniform Distribution (GEUD)” is proposed in this paper by using the idea of Alzaatreh et al. (2013). We investigate its various properties including its characterization, hazard rate, mean residual life, and Shannon entropy. The constraints on its parameters generate three disjoint subfamilies with regard to their distribution characteristics that exhibit more adaptability when compared with other existing models in the literature for fitting real-life datasets with observations limited in the range (0,1). For the estimation of its parameters, we consider and compare the maximum likelihood, distance minimizing Anderson–Darling, and Cramer–von Mises approaches through simulation studies. To display the importance of this model, we provide its important applications. The GEUD model relative to other models is more suitable in describing certain real datasets.