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Activity Number: 419
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #306499
Title: Approximate Tolerance Limits in a One-Way Random Model Using Bootstrap-Based Higher-Order Asymptotics
Author(s): Elizabeth Murrin*+ and Thomas Mathew
Companies: The Johns Hopkins University Applied Physics Laboratory and University of Maryland Baltimore County
Address: 11100 Johns Hopkins Road, Laurel, MD, 20723, United States
Keywords: tolerance limits ; parametric bootstrap ; signed roots of likelihood ratios

The construction of upper or lower tolerance limits is investigated for a one-way random model with balanced or unbalanced data. It is well known the problem reduces to the computation of an upper or lower confidence limit for a suitable percentile of model. The computation of accurate confidence limits is investigated based on two modified versions of the signed root of the log-likelihood ratio test statistic. The modifications are given in DiCiccio, Martin and Stern (2001,Canadian Journal of Statistics), and are implemented using a parametric bootstrap procedure. The proposed tolerance limits are easy to compute, and numerical results indicate that they are accurate. The results will be illustrated with an example.

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