Classical and Bayesian estimation of Weibull distribution in presence of outliers

This study deals with the classical and Bayesian estimation of the parameters of Weibull distribution in presence of outlier. In classical setup, the maximum likelihood estimates of the model parameters along with their standard errors (SEs)and confidence intervals are computed. Bayes estimates along with their posterior SEs and highest posterior density credible intervals of the parameters are also obtained. Markov chain Monte Carlo technique such as Metropolis–Hastings algorithm has been used to simulate sample from the posterior densities of the parameters. Finally, a real data study illustrates the applicability of the proposed model