Confidence Intervals for Reliability of Stress-Strength Models in the Normal Case

DOI10.1080/03610918.2011.560728zbMath1219.62148OpenAlexW1964896347MaRDI QIDQ3087573

Alessandro Barbiero

Publication date: 16 August 2011

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2011.560728

zbMATH Keywords

Monte Carlo simulationsvariance estimationparametric bootstrapasymptotically normalindependent normal

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Parametric tolerance and confidence regions (62F25) Bootstrap, jackknife and other resampling methods (62F40) Statistical tables (62Q05) Reliability and life testing (62N05)

Related Items (3)

Lower confidence limit for reliability based on grouped data using a quantile-filling algorithm ⋮ Data transformation for confidence interval improvement: an application to the estimation of stress-strength model reliability ⋮ Confidence bounds on the coefficient of variation of a normal distribution with applications to win-probabilities

Cites Work

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• Confidence limits for stress-strength reliability involving Weibull models
• Theory and counterexamples for confidence limits on system reliability
• New Approximate Inferential Methods for the Reliability Parameter in a Stress–Strength Model: The Normal Case
• A Distribution-Free Upper Confidence Bound for $\Pr \{Y < X\}$, Based on Independent Samples of $X$ and $Y$
• Reliability For A Bivariate Gamma Distribution
• Missing Data, Imputation, and the Bootstrap

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