Estimation for Kies distribution with generalized progressive hybrid censoring under partially observed competing risks model

Abstract

In this paper, estimation of a competing risks model is discussed with partially observed modes of failure. When latent lifetimes of competing risks follow the Kies distribution under a generalized progressive hybrid censoring, estimation of the unknown parameters is explored under non-order and order restrictions via classical and Bayesian approaches, respectively. The uniqueness and existence of maximum likelihood estimators of parameters are established, and subsequently interval estimators are also derived. Bayesian estimates as well as highest posterior density credible intervals are developed for the model parameters. In addition, when extra order information for the competing risks parameters is available, classical and Bayesian estimates are also established. Extensive simulation studies are conducted to investigate the performance of different methods, and a numerical example is also presented for illustrative purposes.