Comparison of Parameter Estimation Methods in Weibull Distribution

Abstract

The main objective of this study is to compare the parameter estimation methods for Weibull distribution. We consider maximum likelihood and Bayes estimation methods for the scale and shape parameters of Weibull distribution. While computing the Bayes estimates for a Weibull distribution, the continuous conjugate joint prior distribution of the shape and scale parameters does not exist and the closed form expressions of the Bayes estimators cannot be obtained. In this study, we assume that the scale and shape parameters have the exponential prior and they are independently distributed. We use the Lindley approximation and the Markov Chain Monte Carlo (MCMC) method to obtain the approximate Bayes estimators. In simulation study we compare the effectiveness of the parameter estimation methods with Monte Carlo simulations.