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

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.

Keywords:

• Competing risks
• maximum likelihood estimate
• Fisher information matrix
• Bayes estimate
• order restriction

Acknowledgments

The authors would like to thank the referees for their insightful comments that have led to a substantial improvement to an earlier version of the paper. They also thank the Editor and an associate Editor for helpful suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work of Liang Wang is supported by the National Natural Science Foundation of China [grant number 12061091] and the China Postdoctoral Science Foundation [grant number 2019M650260].