Estimation of the Coefficient of Variation of Weibull Distribution Under Type-I Progressively Interval Censoring: A Simulation-Based Approach

Measures of relative variability, such as the Pearson's coefficient of variation (CVₚ), give much insight into the spread of lifetime distributions, like the Weibull distribution. The estimation of the Weibull CVₚ in modern statistics has traditionally been prioritized only when complete data is available. In this article, we estimate the Weibull CVₚ and its second-order alternative, denoted as CVₖ, under type-I progressively interval censoring, which is a typical scenario in survival analysis and reliability theory. Point estimates are obtained using the methods of maximum likelihood, least squares, and the Bayesian approach with MCMC simulation. A nonlinear least squares method is proposed for estimating the CVₚ and CVₖ. We also perform interval estimation of the CVₚ and CVₖ using the asymptotic confidence intervals, bootstrap intervals through the least squares estimates, and the highest posterior density intervals. A comprehensive Monte Carlo simulation study is carried out to understand and compare the performance of the estimators. The proposed least squares and the Bayesian methods produce better point estimates for the CVₚ. The highest posterior density intervals outperform other interval estimates in many cases. The methodologies are also applied to a real dataset to demonstrate the performance of the estimators.