The Odd Log-Logistic Generalized Gamma Model: Properties, Applications, Classical And Bayesian Approach

Abstract

We propose a new lifetime model called the odd log-logistic generalized gamma distri¬bution that can be easily interpreted. Some of its special models are discussed. We obtain general mathematical properties of this distribution including the ordinary moments, and quantile functions. We discuss parameter estimation by the maximum likelihood method and a Bayesian approach, where Gibbs algorithms along with metropolis steps are used to obtain the posterior summaries of interest for survival data with right censoring. Further, for different parameter settings, sample sizes and censoring percentages, we perform various simulations and evaluate the behavior of the estimators. The potentiality of the new distri¬bution is proved by means of two real data sets. In fact, the new distribution can produce better fits than some wellknown distributions