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Estimation and Prediction for a Progressively First-Failure Censored Inverted Exponentiated Rayleigh Distribution

  • Raj Kamal Maurya
  • Yogesh Mani TripathiEmail author
  • Manoj Kumar Rastogi
Original Article
  • 2 Downloads

Abstract

We discuss inverted exponentiated Rayleigh distribution under progressive first-failure censoring. Maximum likelihood and Bayes estimates of unknown parameters are obtained. An expectation–maximization algorithm is used for computing maximum likelihood estimates. Asymptotic intervals are constructed from the observed Fisher information matrix. Bayes estimates of unknown parameters are obtained under the squared error loss function. We construct highest posterior density intervals based on importance sampling. Different predictors and prediction intervals of censored observations are discussed. A Monte Carlo simulations study is performed to compare different methods. Finally, three real data sets are analyzed for illustration purposes.

Keywords

Progressive first-failure censoring Expectation–maximization algorithm Lindley approximation Tirney and Kadane method Importance sampling method HPD intervals 

Mathematics Subject Classification

62N01 62N02 62N05 

Notes

Acknowledgements

The authors are grateful to a reviewer for encouraging comments and constructive suggestions that led to significant improvement in presentation and content of the manuscript. They also thank the Editor for helpful comments. Yogesh Mani Tripathi gratefully acknowledges the partial financial support for this research work under a Grant EMR/2016/001401 Science and Engineering Research Board, India.

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Copyright information

© Grace Scientific Publishing 2019

Authors and Affiliations

  • Raj Kamal Maurya
    • 1
  • Yogesh Mani Tripathi
    • 1
    Email author
  • Manoj Kumar Rastogi
    • 2
  1. 1.Department of MathematicsIndian Institute of Technology PatnaBihtaIndia
  2. 2.Department of StatisticsPatna UniversityPatnaIndia

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