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Inference for Kumaraswamy Distribution Based on Type I Progressive Hybrid Censoring

Author

Listed:
  • Farha Sultana

    (Indian Institute of Technology Patna)

  • Yogesh Mani Tripathi

    (Indian Institute of Technology Patna)

  • Shuo-Jye Wu

    (Tamkang University)

  • Tanmay Sen

    (Indian Institute of Technology Patna)

Abstract

In this paper, we investigate the estimation problems of unknown parameters of the Kumaraswamy distribution under type I progressive hybrid censoring. This censoring scheme is a combination of progressive type I and hybrid censoring schemes. We derive the maximum likelihood estimates of parameters using an expectation-maximization algorithm. Bayes estimates are obtained under different loss functions using the Lindley method and importance sampling procedure. The highest posterior density intervals of unknown parameters are constructed as well. We also obtain prediction estimates and prediction intervals for censored observations. A Monte Carlo simulation study is performed to compare proposed methods and one real data set is analyzed for illustrative purposes.

Suggested Citation

  • Farha Sultana & Yogesh Mani Tripathi & Shuo-Jye Wu & Tanmay Sen, 2022. "Inference for Kumaraswamy Distribution Based on Type I Progressive Hybrid Censoring," Annals of Data Science, Springer, vol. 9(6), pages 1283-1307, December.
    • Handle: RePEc:spr:aodasc:v:9:y:2022:i:6:d:10.1007_s40745-020-00283-z
    DOI: 10.1007/s40745-020-00283-z
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    References listed on IDEAS

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    Cited by:

    1. Weizhong Tian & Liyuan Pang & Chengliang Tian & Wei Ning, 2023. "Change Point Analysis for Kumaraswamy Distribution," Mathematics, MDPI, vol. 11(3), pages 1-22, January.

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