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Prediction of future failures in the log-logistic distribution based on hybrid censored data

Author

Listed:
  • Wassim R. Abou Ghaida

    (Qatar University)

  • Ayman Baklizi

    (Qatar University)

Abstract

We consider the prediction of future observations from the log-logistic distribution. The data is assumed hybrid right censored with possible left censoring. Different point predictors were derived. Specifically, we obtained the best unbiased, the conditional median, and the maximum likelihood predictors. Prediction intervals were derived using suitable pivotal quantities and intervals based on the highest density. We conducted a simulation study to compare the point and interval predictors. It is found that the point predictor BUP and the prediction interval HDI have the best overall performance. An illustrative example based on real data is given.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:4:d:10.1007_s13198-021-01510-3
    DOI: 10.1007/s13198-021-01510-3
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    References listed on IDEAS

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    1. Kundu, Debasis & Mitra, Debanjan, 2016. "Bayesian inference of Weibull distribution based on left truncated and right censored data," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 38-50.
    2. 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.
    3. Mohammad Ali Farsi & S. Masood Hosseini, 2019. "Statistical distributions comparison for remaining useful life prediction of components via ANN," 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(3), pages 429-436, June.
    4. Sampa ChauPattnaik & Mitrabinda Ray & Mitali Madhusmita Nayak, 2021. "Component based reliability prediction," 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. 12(3), pages 391-406, June.
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