Statistical inference of Lomax distribution based on adaptive progressive Type-II hybrid censored competing risks data

Abstract

In this article, statistical inference is taken into account about two-parameter Lomax distribution under adaptive progressive Type-II hybrid censored data in combination with competing risks model. Frequency and Bayesian estimators under both symmetric and asymmetric loss functions are obtained for unknown parameters as well as cause-special reliability and hazard functions. Furthermore, the existence and uniqueness of maximum likelihood estimators are given. The corresponding confidence and credible intervals are also constructed based on asymptotic theory, delta method and MCMC technique. According to the simulation results, the performance of all the proposed point and interval estimates is evaluated. Finally, an example by analyzing real experimental data is given to illustrate all the deductive process established in this article.

Keywords:

• Maximum likelihood estimation
• Bayesian method
• adaptive progressive Type-II hybrid censoring
• competing risks model
• MCMC technique

MATHEMATICS SUBJECT CLASSIFICATION:

• 62N02

Acknowledgements

The authors' work was partially supported by the Fundamental Research Funds for the Central Universities (2020YJS187). The authors would like to thank the editor and anonymous referees for their constructive comments and suggestions that have substantially improved the original manuscript.