Pivotal and Bayesian inference in exponential coherent systems under progressive censoring

Document Type : Original Paper

Authors

1 Department of Statistics, Payam Noor, Tehran, Iran

2 Department of Statistics, Mazandaran University, Mazandaran, Iran

Abstract

In this paper, statistical inference is considered for k-component coherent systems, when the system lifetime data is progressively type-II censored. In these coherent systems, it is assumed that the system structure and system signature are known and the component lifetime distribution is exponential. Pivotal and Bayesian methods are introduced for point estimation of the component lifetime parameter, and these methods are compared with the maximum likelihood and the least squares methods existing in the literature. Pivotal confidence interval, Bayesian confidence interval and confidence interval based on the likelihood ratio test are computed. Using Monte Carlo simulations, different point and interval estimates are compared and it is observed that pivotal and Bayesian methods perform better than other existing estimation methods.

Keywords

Main Subjects


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Volume 11, Issue 3
September 2021
Pages 496-514
  • Receive Date: 26 December 2020
  • Revise Date: 21 March 2021
  • Accept Date: 15 August 2021
  • First Publish Date: 24 August 2021