Comparison Of Maximum Likelihood And Bayes Estimators Under Symmetric And Asymmetric Loss Functions By Means Of Tierney Kadane’s Approximation For Weibull Distribution

Abstract

In this study, it is considered the problem of comparing the performances of the Maximum Likelihood (ML) and Bayes estimators under symmetric and asymmetric loss function for the unknown parameters of Weibull distribution. ML estimators are computed by using the Newton Raphson method. Bayesian estimations under Squared, Linex and General Entropy loss functions by using Jeffrey’s extension prior are introduced with Tierney Kadane approximation for Weibull distribution. For different sample sizes, estimators are compared to obtain the best estimator in terms of mean squared errors using a Monte Carlo simulation study.