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Approximate Tolerance Limits Under Log-Location-Scale Regression Models in the Presence of Censoring

Summary: [This abstract is based on the authors' abstract.] Most methodologies for setting tolerance limits in life test experiments focus on one-sample problems. A method constructing approximate tolerance limits is proposed under log-location-scale regression models that are widely used in reliability and life test experiments. The method is based on the use of the large sample theory of maximum likelihood estimators modified by a technique to enhance small sample accuracy. Approximate tolerance limits are shown to have nominal coverage probability under the assumption of independent censoring.

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  • Topics: Process Capability
  • Keywords: Jackknife, Life testing, Maximum likelihood estimate (MLE), Regression analysis
  • Author: Emura, Takeshi; Wang, Hsiuying
  • Journal: Technometrics