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Inference for an Inverted Exponentiated Pareto Distribution Under Progressive Censoring

  • Raj Kamal Maurya
  • Yogesh Mani Tripathi
  • Tanmay Sen
  • Manoj Kumar RastogiEmail author
Original Article
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Abstract

In this paper, estimation of unknown parameters of an inverted exponentiated Pareto distribution is considered under progressive Type-II censoring. Maximum likelihood estimates are obtained from the expectation–maximization algorithm. We also compute the observed Fisher information matrix. In the sequel, asymptotic and bootstrap-p intervals are constructed. Bayes estimates are derived using the importance sampling procedure with respect to symmetric and asymmetric loss functions. Highest posterior density intervals of unknown parameters are constructed as well. The problem of one- and two-sample prediction is discussed in Bayesian framework. Optimal plans are obtained with respect to two information measure criteria. We assess the behavior of suggested estimation and prediction methods using a simulation study. A real dataset is also analyzed for illustration purposes. Finally, we present some concluding remarks.

Keywords

Expectation–maximization algorithm Bootstrap interval Importance sampling method HPD interval Bayes prediction Optimal censoring 

Notes

Acknowledgements

The authors are thankful to the reviewers for their valuable suggestions which have significantly improved the content and the presentation of our paper. They also thank the Editor and an Associate Editor for the encouraging comments. Yogesh Mani Tripathi gratefully acknowledges the partial financial support for this research work under a Grant EMR/2016/001401 SERB, India.

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Copyright information

© Grace Scientific Publishing 2018

Authors and Affiliations

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

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