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Predicting future failure times by using quantile regression

Author

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  • Jorge Navarro

    (Universidad de Murcia)

  • Francesco Buono

    (Università di Napoli Federico II)

Abstract

The purpose of the paper is to study how to predict the future failure times in a sample from the early failures (type II censored data). We consider both the case of independent and dependent lifetimes. In both cases we assume identically distributed random variables. To predict the future failures we use quantile regression techniques that also provide prediction regions for them. Some illustrative examples show how to apply the theoretical results to simulated and real data sets.

Suggested Citation

  • Jorge Navarro & Francesco Buono, 2023. "Predicting future failure times by using quantile regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(5), pages 543-576, July.
    • Handle: RePEc:spr:metrik:v:86:y:2023:i:5:d:10.1007_s00184-022-00884-z
    DOI: 10.1007/s00184-022-00884-z
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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. J. F. Lawless & Marc Fredette, 2005. "Frequentist prediction intervals and predictive distributions," Biometrika, Biometrika Trust, vol. 92(3), pages 529-542, September.
    4. Jorge Navarro & Camilla Calì & Maria Longobardi & Fabrizio Durante, 2022. "Distortion representations of multivariate distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 925-954, October.
    5. Omar M. Bdair & Mohammad Z. Raqab, 2022. "Prediction of future censored lifetimes from mixture exponential distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(7), pages 833-857, October.
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