Statistical inference for modified Weibull distribution based on progressively type-II censored data

Abstract

In the context of survival and medical studies, it sounds more natural to have situations where the removal of units prior to failure is preplanned for cost or money constraints. Here in this paper, we consider the inference problem including estimation and prediction for three-parameter modified Weibull distribution based on progressively type-II censored sample data. The maximum likelihood and Bayes approaches based on conjugate and discrete priors for estimating the model parameters are derived. These Bayes estimators are developed and computed using the balanced square error and balanced LINEX loss functions. Approximate confidence intervals and credible intervals of the model parameters are also performed. The point predictors and credible intervals of unobserved units based on an informative progressive type-II censored data in one-sample and two-sample prediction problems are also developed. Monte Carlo simulations are performed for comparison purposes and one real data set is analyzed for illustrative purposes.