Bayesian and Maximum Likelihood Estimation for Mixture Models of the New Topp-Leone-G Family

This paper introduces a new family of continuous distributions, the mixture of two components from the new Topp-Leone-G family. Statistical properties of the proposed family are explored, with a focus on the mixture of two new Topp-Leone exponential distributions as a sub-model. The study uses maximum likelihood and Bayesian methods under Type-II censoring to estimate the unknown parameters, reliability, and hazard rate functions. A simulation study evaluates the performance of the estimators. Finally, two real data sets are utilized to validate the simulated results and demonstrate the practical applicability of the proposed distribution in real life.