Inference of progressively censored competing risks data from Kumaraswamy distributions

A competing risks model based on Kumaraswamy distribution is discussed under progressive censoring. When the latent lifetime model of failure causes features different and common parameters, maximum likelihood estimates for unknown parameters are established where the existence and uniqueness of the estimates are provided, and the approximate confidence intervals are also constructed via the observed fisher information matrix. Moreover, Bayes estimates and associated highest posterior density credible intervals are also obtained based on Monte-Carlo Markov chain sampling methods. In addition, to test the equivalence of parameters between the competing risks, likelihood ratio test is also proposed. Finally, simulation studies and real-life example are presented for illustration purpose. (C) 2018 Elsevier B.V. All rights reserved.