Reliability estimation in stress–strength models: an MCMC approach

DOI10.1080/02331888.2011.637629zbMath1440.62362OpenAlexW2102634391MaRDI QIDQ2863066

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Publication date: 21 November 2013

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2011.637629

zbMATH Keywords

Bayes estimatorasymptotic distributionsmaximum-likelihood estimatorMCMC methodsstress-strength modelbootstrap confidence intervalsmodified Weibull distribution

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Parametric tolerance and confidence regions (62F25) Point estimation (62F10) Monte Carlo methods (65C05) Estimation in survival analysis and censored data (62N02) Reliability and life testing (62N05)

Related Items (10)

Prediction under an adaptive progressive type-II censoring scheme for Burr Type-XII distribution ⋮ Estimation of generalized exponential distribution based on an adaptive progressively type-II censored sample ⋮ Reliability analysis of Birnbaum–Saunders model based on progressive type-II censoring ⋮ Reliability estimation in multicomponent stress–strength model for Topp-Leone distribution ⋮ Estimating a new stress–strength index for several exponential populations with a common location ⋮ Reliability estimation of stress-strength model using finite mixture distributions under progressively interval censoring ⋮ Inferences on stress–strength reliability based on ranked set sampling data in case of Lindley distribution ⋮ Statistical inferences for stress–strength in the proportional hazard models based on progressive Type-II censored samples ⋮ On estimation of stress–strength parameter using record values from proportional hazard rate models ⋮ Unnamed Item

Cites Work

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• Markov chains for exploring posterior distributions. (With discussion)
• Markov chain Monte Carlo methods for parameter estimation of the modified Weibull distribution
• A Bayes analysis of modified Weibull distribution via Markov chain Monte Carlo simulation
• Unbiased estimation ofP(X>Y) for two-parameter exponential populations using order statistics
• Estimation of Pr(X < Y) Using Record Values in the One and Two Parameter Exponential Distributions
• Monte Carlo sampling methods using Markov chains and their applications

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