Inference for constant-stress accelerated degradation test based on gamma process

DOI10.1016/j.apm.2018.10.017zbMath1481.62077OpenAlexW2897311423WikidataQ129082015 ScholiaQ129082015MaRDI QIDQ2307203

Peihua Jiang, Bing Xing Wang, Fang Tao Wu

Publication date: 27 March 2020

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2018.10.017

zbMATH Keywords

maximum likelihoodCornish-Fisher expansiongamma processgeneralized confidence intervalaccelerated degradationdegradation mechanism

Mathematics Subject Classification ID

Processes with independent increments; Lévy processes (60G51) Parametric tolerance and confidence regions (62F25) Estimation in survival analysis and censored data (62N02)

Related Items (9)

Modeling of degradation data via Wiener stochastic process based on acceleration factor constant principle ⋮ Optimal plan for Wiener constant-stress accelerated degradation model ⋮ Acceleration invariance principle for Hougaard processes in degradation analysis ⋮ Statistical analysis for the doubly accelerated degradation Wiener model: an objective Bayesian approach ⋮ Bias reduction in the two-stage method for degradation data analysis ⋮ Reliability and maintenance of systems subject to gamma degradation and shocks in dynamic environments ⋮ Degradation data analysis based on gamma process with random effects ⋮ Inverse Gaussian process based reliability analysis for constant-stress accelerated degradation data ⋮ Fiducial inference-based failure mechanism consistency analysis for accelerated life and degradation tests

Uses Software

• SPLIDA

Cites Work

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• Modeling the Mileage Accumulation Process with Random Effects
• Nonparametric estimation of the shape function in a Gamma process for degradation data
• Accelerated Testing
• Using Degradation Measures to Estimate a Time-to-Failure Distribution
• Optimal design of accelerated degradation tests based on Wiener process models
• Exact confidence intervals for the shape parameter of the gamma distribution

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