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Estimation and prediction using classical and Bayesian approaches for Burr III model under progressive type-I hybrid censoring

Author

Listed:
  • Sukhdev Singh

    (Indian Institute of Technology Patna
    Chandigarh University)

  • Reza Arabi Belaghi

    (University of Tabriz)

  • Mehri Noori Asl

    (University of Tabriz)

Abstract

In this paper we address the problems of estimation and prediction when lifetime data following Burr type III distribution are observed under progressive type-I hybrid censoring. We first obtain maximum likelihood estimators of unknown parameters using expectation maximization and stochastic expectation maximization algorithms, and associated interval estimates using Fisher information matrix. We then obtain Bayes estimators based on non-informative and informative priors under squared error, entropy and Linex loss functions using the method of Tierney–Kadane and importance sampling technique, and associated highest posterior density interval estimates by making use of Chen and Shao method. We further predict the censored observations and interval estimates under classical and Bayesian approaches. Finally we analyze two real data sets, and conduct a simulation study to compare the performance of various proposed estimators and predictors.

Suggested Citation

  • Sukhdev Singh & Reza Arabi Belaghi & Mehri Noori Asl, 2019. "Estimation and prediction using classical and Bayesian approaches for Burr III model under progressive type-I hybrid censoring," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 746-764, August.
    • Handle: RePEc:spr:ijsaem:v:10:y:2019:i:4:d:10.1007_s13198-019-00806-9
    DOI: 10.1007/s13198-019-00806-9
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    References listed on IDEAS

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    1. Sukhdev Singh & Yogesh Tripathi, 2015. "Reliability sampling plans for a lognormal distribution under progressive first-failure censoring with cost constraint," Statistical Papers, Springer, vol. 56(3), pages 773-817, August.
    2. Kundu, Debasis & Joarder, Avijit, 2006. "Analysis of Type-II progressively hybrid censored data," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2509-2528, June.
    3. Sukhdev Singh & Yogesh Mani Tripathi, 2018. "Estimating the parameters of an inverse Weibull distribution under progressive type-I interval censoring," Statistical Papers, Springer, vol. 59(1), pages 21-56, March.
    4. Nizar Bouguila & Jian Han Wang & A. Ben Hamza, 2010. "Software modules categorization through likelihood and bayesian analysis of finite dirichlet mixtures," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 235-252.
    5. Erhard Cramer & George Iliopoulos, 2010. "Adaptive progressive Type-II censoring," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(2), pages 342-358, August.
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    Cited by:

    1. Wassim R. Abou Ghaida & Ayman Baklizi, 2022. "Prediction of future failures in the log-logistic distribution based on hybrid censored data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1598-1606, August.

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