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    Author(s): Steve VerrillVictoria L. Herian; David W. Green
    Date: 2002
    Source: Proceedings, 2001 American Statistical Association Annual Meeting, Physical Sciences and Engineering Section, 2001 August 5-9, Atlanta, GA. Alexandria, VA : American Statistical Association, 2002.
    Publication Series: Miscellaneous Publication
    PDF: View PDF  (134 KB)

    Description

    Predictor sort experiments attempt to make use of the correlation between a predictor that can be measured prior to the start of an experiment and the response variable that we are investigating. Properly designed and analyzed, they can reduce necessary sample sizes, increase statistical power, and reduce the lengths of confidence intervals. However, if the non- random nature of the predictor sort is not taken into account, problems can occur. In particular, standard one-sided lower confidence bounds on quantiles of a normal distribution are overly conservative in a predictor sort situation. For lumber strength applications, this leads to “allowable properties” that are too low. We have developed asymptotic theory that yields the correct k value in the y - ks approach to obtaining a confidence bound. The resulting confidence bounds have coverages that approach the nominal values faster than bounds based on maximum likelihood estimation. In a subsequent paper we will provide k values that are appropriate for small sample sizes.

    Publication Notes

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    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    Verrill, Steve; Herian, Victoria L.; Green, David W. 2002. Predictor sort sampling and one-sided confidence bounds on quantiles. Proceedings, 2001 American Statistical Association Annual Meeting, Physical Sciences and Engineering Section, 2001 August 5-9, Atlanta, GA. Alexandria, VA : American Statistical Association, 2002.

    Keywords

    Tolerance bounds, asymptotics

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