Statistical Inference for the Extended Weibull Distribution Based on Adaptive Type-II Progressive Hybrid Censored Competing Risks Data
Keywords:Maximum likelihood estimation, Bayesian estimation, bootstrap confidence intervals, extended Weibull model, Markov chain Monte Carlo
This paper discussed the unknown parameters of extended Weibull distribution under adaptive type II progressive hybrid censoring scheme (AT-II PHCS) in the existence of the competing risks model. Depending on this scheme the maximum likelihood and Bayesian estimators of the distribution parameters are obtained. Bayes estimators have been developed using the standard Bayes method under square error, using gamma prior for the parameter. Also, the asymptotic confidence intervals and two bootstrap conference intervals are offered. As a final point, the maximum likelihood, bootstrap and Bayes estimates are set in a comparison via a Monte Carlo simulation study. Finally, a set of real data is used to test the hypothesis that the causes of failure follow extended Weibull distribution.