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.

Interval estimators for reliability : the bivariate normal case / A. Barbiero. - In: JOURNAL OF APPLIED STATISTICS. - ISSN 0266-4763. - 39:3(2012), pp. 501-512.

Interval estimators for reliability : the bivariate normal case

A. Barbiero
Primo
2012

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.
approximate estimators; Monte Carlo simulations; parametric bootstrap; stress-strength models; variance estimation
Settore SECS-S/01 - Statistica
2012
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/169741
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