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Interval estimators for reliability: the bivariate normal case

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  • Alessandro Barbiero

Abstract

This paper proposes procedures to provide confidence intervals (CIs) for reliability in stress--strength models, considering the particular case of a bivariate normal set-up. The suggested CIs are obtained by employing either asymptotic variances of maximum-likelihood estimators or a bootstrap procedure. The coverage and the accuracy of these intervals are empirically checked through a simulation study and compared with those of another proposal in the literature. An application to real data is provided.

Suggested Citation

  • Alessandro Barbiero, 2012. "Interval estimators for reliability: the bivariate normal case," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 501-512, June.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:501-512
    DOI: 10.1080/02664763.2011.602055
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    Cited by:

    1. Joby K. Jose & M. Drisya, 2020. "Time-dependent stress–strength reliability models based on phase type distribution," Computational Statistics, Springer, vol. 35(3), pages 1345-1371, September.
    2. Nabakumar Jana & Somesh Kumar & Kashinath Chatterjee, 2016. "Bayes estimation for exponential distributions with common location parameter and applications to multi-state reliability models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(15), pages 2697-2712, November.

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