Inference for a general family of inverted exponentiated distributions with partially observed competing risks under generalized progressive hybrid censoring

Abstract

In this paper, statistical inference for a competing risks model is discussed when latent failure times belong to a general family of inverted exponentiated distributions. Based on a generalized progressive hybrid censored data with partially observed failure causes, estimations for unknown parameters are presented under nonrestricted and restricted parameter cases from classic and Bayesian perspectives, respectively. The existence and uniqueness of maximum likelihood estimators of the unknown parameters are established, and the associated approximate confidence intervals are also constructed via Fisher information matrix. In sequel, the Bayes estimators and credible intervals of the parameters are also obtained as well. Finally, the performance of different estimators are evaluated using Monte Carlo simulations and a real data set is also analyzed for illustration.

Keywords:

• Competing risk
• Bayes estimate
• generalized progressive hybrid censoring
• inverted exponentiated distributions
• maximum likelihood estimate
• order restriction

Acknowledgements

The authors would like to thank the editor and the referees for their insightful comments that have led to a substantial improvement to an earlier version of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work of Liang Wang was supported by the National Natural Science Foundation of China (No. 12061091), the Yunnan Fundamental Research Projects in Year of 2021 and the China Postdoctoral Science Foundation (No. 2019M650260).