Optimum step‐stress accelerated life test plans for log‐location‐scale distributions

DOI10.1002/nav.20299zbMath1152.62381OpenAlexW2105615011MaRDI QIDQ5503764

William Q. Meeker, Haiming Ma

Publication date: 16 January 2009

Published in: Naval Research Logistics (NRL) (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/nav.20299

zbMATH Keywords

maximum likelihoodcumulative exposure modellarge-sample approximate variance

Mathematics Subject Classification ID

Related Items (12)

Optimum step-stress accelerated degradation test for Wiener degradation process under constraints ⋮ Inference on constant stress accelerated life tests for log-location-scale lifetime distributions with type-I hybrid censoring ⋮ Optimal Robust Designs of Step-Stress Accelerated Life Testing Experiments for Proportional Hazards Models ⋮ Bayesian planning of optimal step-stress accelerated life test for log-location-scale distributions ⋮ How do heterogeneities in operating environments affect field failure predictions and test planning? ⋮ Planning step-stress test plans under type-I hybrid censoring for the log-location-scale distribution ⋮ Unnamed Item ⋮ Bayesian order-restricted inference of a Weibull multi-step step-stress model ⋮ Estimation in step-stress accelerated life tests for the exponentiated exponential distribution with type-I censoring ⋮ Planning step-stress test plans under Type-I censoring for the log-location-scale case ⋮ Estimating the Pareto parameters under progressive censoring data for constant-partially accelerated life tests ⋮ Optimal designs for step-stress models under interval censoring

Cites Work

• Planning Accelerated Life Tests with Two or More Experimental Factors
• Optimum simple step-stress accelerated life tests with censoring
• Optimum Simple Step-Stress Plans for Accelerated Life Testing
• Accelerated Life Testing - Step-Stress Models and Data Analyses
• Accelerated Testing
• Theory for Optimum Censored Accelerated Life Tests for Normal and Lognormal Life Distributions
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