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Parameter estimation of the inverted exponentiated Rayleigh distribution based on progressively first-failure censored samples

  • Shuo Gao
  • Wenhao GuiEmail author
Original Article
  • 50 Downloads

Abstract

We study statistical inference of the inverted exponentiated Rayleigh distribution under progressively first-failure censoring samples in our paper. Specifically, we deal with Maximum likelihood and Bayes estimators of parameters. The observed Fisher matrix is conducive to obtain asymptotic confidence interval. Parametric bootstrap methods are applied to provide the confidence intervals. Bayes estimators in terms of squared error loss function are derived with Metropolis–Hastings technique, which are helpful to construct highest posterior density credible intervals. We compare the behavior of various estimators by conducting Monte Carlo simulations. A set of actual data is analyzed to introduce the proposed methods.

Keywords

Maximum likelihood estimation Parametric bootstrap Metropolis–Hastings algorithm Progressively first-failure censoring 

Notes

Acknowledgements

Funding was provided by the program for the Fundamental Research Funds for the Central Universities (Grant No. 2014RC042).

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Copyright information

© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2019

Authors and Affiliations

  1. 1.Department of MathematicsBeijing Jiaotong UniversityBeijingChina

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