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Optimal prediction regions of future lifetimes under Type-II censored samples

Author

Listed:
  • Mohammad Z. Raqab

    (Kuwait University
    The University of Jordan)

  • S. F. Bagheri

    (University of Mazandaran)

  • A. Asgharzadeh

    (University of Mazandaran)

  • Ahmad N. Alothman

    (Kuwait University)

Abstract

Based on a Type-II censored data from exponential distribution, Bagheri et al. (IEEE Trans Reliab 71(1):100–110, 2022) introduced the joint prediction of future failure times based on Type-II censored data from the exponential distribution. In fact, they developed pivotal quantities for obtaining prediction regions of two future failure times using the stochastic independence of the spacings of increments of order statistics along with a scaling transformation. In this work, we consider the same problem of joint prediction where the prediction likelihood function is used to produce pivotal quantities for obtaining prediction region of two failure times. Based on these pivotal quantities, the optimal prediction regions of the joint failure times are derived. A Monte Carlo simulation study is performed to assess the so developed prediction regions and compare them with ones obtained by Bagheri et al. (IEEE Trans Reliab 71(1):100–110, 2022). Through simulation, it is evident that our joint prediction regions are highly competitive efficiencies compared to their existing counterparts. Further, two real life data sets are analyzed to explain our procedures presented.

Suggested Citation

  • Mohammad Z. Raqab & S. F. Bagheri & A. Asgharzadeh & Ahmad N. Alothman, 2025. "Optimal prediction regions of future lifetimes under Type-II censored samples," Statistical Papers, Springer, vol. 66(5), pages 1-25, August.
    • Handle: RePEc:spr:stpapr:v:66:y:2025:i:5:d:10.1007_s00362-025-01729-3
    DOI: 10.1007/s00362-025-01729-3
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    References listed on IDEAS

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    1. Elham Basiri & Jafar Ahmadi & Mohammad Z. Raqab, 2016. "Comparison among non parametric prediction intervals of order statistics," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(9), pages 2699-2713, May.
    2. El-Adll, Magdy E., 2011. "Predicting future lifetime based on random number of three parameters Weibull distribution," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(9), pages 1842-1854.
    3. Narayanaswamy Balakrishnan & Ritwik Bhattacharya, 2022. "D-optimal joint best linear unbiased prediction of order statistics," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(2), pages 253-267, February.
    4. J. F. Lawless & Marc Fredette, 2005. "Frequentist prediction intervals and predictive distributions," Biometrika, Biometrika Trust, vol. 92(3), pages 529-542, September.
    5. Basak, Indrani & Basak, Prasanta & Balakrishnan, N., 2006. "On some predictors of times to failure of censored items in progressively censored samples," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1313-1337, March.
    6. Kotb, M.S. & Raqab, M.Z., 2019. "Statistical inference for modified Weibull distribution based on progressively type-II censored data," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 162(C), pages 233-248.
    7. A. Asgharzadeh & S. F. Bagheri & N. A. Ibrahim & M. R. Abubakar, 2020. "Optimal confidence regions for the two-parameter exponential distribution based on records," Computational Statistics, Springer, vol. 35(1), pages 309-326, March.
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