On partially observed competing risk model under generalized progressive hybrid censoring for Lomax distribution. Issue 5 (3rd September 2022) - Mahto, Amulya Kumar; Lodhi, Chandrakant; Tripathi, Yogesh Mani; Wang, Liang
ABSTRACT: We study a competing risks model under the assumption that latent failure times follow family of Lomax distributions. We obtain various inferences for model parameters when causes of failure are partially known and lifetime data are observed using a generalized progressive hybrid censoring scheme. The existence and uniqueness properties of maximum likelihood estimators of unknown parameters are established. Bayes estimators and associated credible intervals are also obtained. In addition, various inferences for unknown parameters are derived under order-restricted shape parameters of Lomax distributions. Finally, a simulation study is conducted to evaluate the performance of the proposed estimates. A real data set is also analysed for illustration purposes.