Inference for partially observed competing risks model for Kumaraswamy distribution under generalized progressive hybrid censoring

Amulya Kumar Mahto, Chandrakant Lodhi, Yogesh Mani Tripathi and Liang Wang

Journal of Applied Statistics, 2022, vol. 49, issue 8, 2064-2092

Abstract: In this paper, inference for a competing risks model is studied when latent failure times follow Kumaraswamy distribution and causes of failure are partially observed. Under generalized progressive hybrid censoring, existence and uniqueness of maximum likelihood estimators of model parameters are established. The confidence intervals are obtained by using asymptotic distribution theory. We further compute Bayes estimators along with credible intervals. In addition, inference is also discussed when there is order restricted shape parameters. The performance of all estimates is investigated using Monte-Carlo simulations. Finally, analysis of a real data set is presented for illustration purposes.

Date: 2022
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DOI: 10.1080/02664763.2021.1889999

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