Unit Xgamma Distribution: Its Properties, Estimation and Application

Abstract

A new one-parameter model for unit-interval datasets is introduced. The proposed distribution is termed “Unit Xgamma distribution.” Some mathematical properties of the new distribution are derived. We also characterize it using truncated moments and a hazard function. Maximum likelihood, least-squares, weighted least-squares, Anderson- Darling, Cramer-von Mises, and maximum product spacing are among the five estimation methods used to estimate the parameter. A Monte Carlo simulation was used to test the efficacy of these developed estimators. The flexibility of the proposed distribution was assessed using water capacity data. The proposed unit Xgamma distribution can be used for bounded datasets as an alternative to the well-known competitive distributions available in the literature.