Analysis of Progressive Type-II Competing Risks Data, with Applications

In this study, a competing risk (CR) model is studied under progressive Type-II censoring (PTIIC) lifetimes having the inverted Topp–Leone distribution. The latent causes of failure are presumed to be independent. The process of estimating the unknown parameters is performed using the maximum likelihood (ML) and Bayesian methods. The Bayesian estimators are derived from gamma and uniform priors under various loss functions. The ML estimate is used to create confidence intervals. In addition, we present two bootstrap confidence intervals for unknown parameters. Further, credible confidence intervals and highest posterior density intervals are constructed based on the marginal posterior distribution. Monte Carlo simulation is used to examine the performance of different estimates. Real data applications are used to confirm the proposed estimates as well as compare the proposed model with other distributions for complete data and CR under PTIIC.