Inference for a general family of inverted exponentiated distributions with partially observed competing risks under generalized progressive hybrid censoring
Chandrakant Lodhi, Yogesh Mani Tripathi, Liang Wang
Abstract
In this paper, statistical inference for a competing risks model is discussed when latent failure times belong to a general family of inverted exponentiated distributions. Based on a generalized progressive hybrid censored data with partially observed failure causes, estimations for unknown parameters are presented under nonrestricted and restricted parameter cases from classic and Bayesian perspectives, respectively. The existence and uniqueness of maximum likelihood estimators of the unknown parameters are established, and the associated approximate confidence intervals are also constructed via Fisher information matrix. In sequel, the Bayes estimators and credible intervals of the parameters are also obtained as well. Finally, the performance of different estimators are evaluated using Monte Carlo simulations and a real data set is also analyzed for illustration.