Parameter Estimation of Type-II Hybrid Censored Weighted Exponential Distribution

Abstract

A hybrid censoring scheme is a mixture of Type-I and Type-II censoring schemes. We study the estimation of parameters of weighted exponential distribution based on Type-II hybrid censored data. By applying the EM algorithm, maximum likelihood estimators are evaluated. Using Fisher information matrix, asymptotic confidence intervals are provided. By applying Markov chain Monte Carlo techniques, Bayes estimators, and corresponding highest posterior density confidence intervals of parameters are obtained. Monte Carlo simulations are performed to compare the performances of the different methods, and one dataset is analyzed for illustrative purposes.

Keywords:

• Asymptotic distribution
• Bayes estimators
• EM algorithm
• Hybrid censoring
• Markov chain Monte Carlo
• Maximum likelihood estimators
• Type II censoring
• Type-I censoring

Mathematics Subject Classification:

• 62F10
• 62F15
• 62N02