Estimation under inverse Weibull distribution based on Balakrishnan's unified hybrid censored scheme

In this article, point and interval estimations of the parameters and of the inverse Weibull distribution (IWD) have been studied based on Balakrishnan's unified hybrid censoring scheme (UHCS), see Balakrishnan etal. In point estimation, the maximum likelihood (ML) and Bayes (B) methods have been used. The Bayes estimates have been computed based on squared error loss (SEL) function and Linex loss function and using Markov Chain Monte Carlo (MCMC) algorithm. In interval estimation, a (1 - ) x 100% approximate, bootstrap-p, credible and highest posterior density (HPD) confidence intervals (CIs) for the parameters and have been introduced. Based on Monte Carlo simulation, Bayes estimates have been compared with their corresponding maximum likelihood estimates by computing the mean squared errors (MSEs) of all estimators. Finally, point and interval estimations of all parameters have been studied based on a real data set as an illustrative example.