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Updated: Sep 6, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Inference for partially observed competing risks model for Kumaraswamy distribution under generalized progressive
Amulya Kumar Mahto1, Chandrakant Lodhi2, Yogesh Mani Tripathi1
1Department of Mathematics, Indian Institute of Technology, Patna, India.
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.
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