Classical and Bayesian Inference of Marshall-Olkin Extended Gompertz Makeham Model with Modeling of Physics Data

Abstract

The purpose of this study is to present the Marshall- Olkin extended Gompertz Makeham $\left(\text{MOEGM}\right)$ lifetime distribution, which has four parameters. As a result, we will describe some of the structural elements that are introduced for this model. The maximum likelihood approach is used to estimate the model parameters, and it is well known that likelihood estimators for unknown parameters are not always available. As a result, we examine the prior distributions, which allow for prior dependence among the components of the parameter vector, as well as the Bayesian estimators derived with respect to the squared error loss function. A Monte Carlo simulation research is carried out to examine the performance of the likelihood estimators and the Bayesian technique. Finally, we demonstrate the significance of the new model. And to conclude, we illustrate the importance of the new model by exploring some of the empirical applications of physics to show it’s flexibility and potentiality of a new model.