Statistical inference and prediction for the Weibull process with incomplete observations

DOI10.1016/j.csda.2007.05.003zbMath1452.62737OpenAlexW2087235954MaRDI QIDQ1023486

Man-Lai Tang, Jun-Wu Yu, Guo-Liang Tian

Publication date: 12 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.05.003

zbMATH Keywords

confidence intervalsnonhomogeneous Poisson processAMSAA modelreliability growthgoodness-of-fit testWeibull processprediction limits

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Parametric tolerance and confidence regions (62F25) Reliability and life testing (62N05)

Related Items (13)

On Crevecoeur's bathtub-shaped failure rate model ⋮ Bayesian prior information fusion for power law process via evidence theory ⋮ Prediction in trend-renewal processes for repairable systems ⋮ Inference for a gradually deteriorating system with imperfect maintenance ⋮ Markov chain Monte Carlo methods for parameter estimation of the modified Weibull distribution ⋮ Robust estimation for the Weibull process applied to eruption records ⋮ Computing tolerance limits for the lifetime of a \(k\)-out-of-\(n\):\(F\) system based on prior information and censored data ⋮ Generalized Confidence Interval for the Scale Parameter of the Power-Law Process ⋮ Generalized confidence interval for the scale parameter of the power-law process with incomplete failure data ⋮ Nonparametric estimation and bootstrap confidence intervals for the optimal maintenance time of a repairable system ⋮ Inference for Weibull competing risks model with partially observed failure causes under generalized progressive hybrid censoring ⋮ Reliability inference and sample-size determination under double censoring for some two-parameter models ⋮ Modified signed log-likelihood ratio test for the scale parameter of the power-law process with applications to repairable systems

Cites Work

• Bayesian inference for the power law process
• Bayesian Computation for Nonhomogeneous Poisson Processes in Software Reliability
• Confidence Interval Procedures for the Weibull Process with Applications to Reliability Growth
• Inferences on the Parameters and Current System Reliability for a Time Truncated Weibull Process
• Some Results on Inference for the Weibull Process
• Prediction Intervals for the Weibull Process
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