Inference of partially observed causes for failure of Lomax competing risks model under type-II generalized hybrid censoring scheme

Abstract

Time-to- failure under different causes of failure is known as a competing risks model. Practice, competing risks data can be appeared in different applications such as engineering fields or biological and medical lifetime studies as well as other related areas. Also, the causes of failure, which are competing may be partially observed. In this paper, we adopted the competing risks model with partially observed causes of failure when the latent failure times follow Lomax life distribution under type-II generalized hybrid censoring scheme. The maximum likelihood estimators of the model parameters with the associated confidence intervals are discussed. Moreover, Bayes estimators under importance sampling procedure with probability credible intervals are developed. The results are discussed using both real and simulated data sets for illustration purposes. Finally, the Monte Carlo simulation experiments are performed to assess and compare the different proposed methods with some brief comments.