Bayesian Modeling for a Shape Parameter of Weibull-Lomax Distribution with an Application to Health Data

In this paper, effective Bayesian estimation procedures have been explored to estimate a shape parameter of the Weibull-Lomax distribution with the aid of classical and Bayesian approaches under informative prior distribution. This approach includes the square error loss function (SELF), precautionary loss function (PLF), quadratic loss function (QLF), general entropy loss function (GELF) and linex loss function (LLF) for optimal estimate of the shape parameter. To validate the theoretical results, an extensive Monte Carlo simulations and a real health data are considered for an illustration of comparative study of classical and Bayesian approach. The mean square error (MSE) are also derived to show the efficiency of the estimators. Further cumsum plots and iterative plots are demonstrated to justify convergence of precision of the estimates.