On partially observed competing risks model for Chen distribution under generalized progressive hybrid censoring

Kundan Singh, Amulya Kumar Mahto and Yogesh Mani Tripathi

Statistica Neerlandica, 2024, vol. 78, issue 1, 105-135

Abstract: In this paper, we discuss the inference for the competing risks model when the failure times follow Chen distribution. With assumption of two causes of failures, which are partially observed, are considered as independent. The existence and uniqueness of maximum likelihood estimates for model parameters are obtained under generalized progressive hybrid censoring. Also, we discussed the classical and Bayesian inferences of the model parameters under the assumption of restricted and nonrestricted parameters. Performance of classical point and interval estimators are compared with Bayesian point and interval estimators by conducting extensive simulation study. In addition to that, for illustration purpose, a real life example is discussed. Finally, some concluding remarks, regarding the presented model, are made.

Date: 2024
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