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Activity Number:
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510
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #308429 |
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Title:
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A Parametric Bootstrap Approach for ANOVA with Unequal Variances: Fixed and Random Models
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Author(s):
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Fei Lu*+ and Kalimuthu Krishnamoorthy and Thomas Mathew
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Companies:
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University of Louisiana at Lafayette and University of Louisiana at Lafayette and University of Maryland, Baltimore County
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Address:
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POBox 40678, Lafayette, LA, 70504,
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Keywords:
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Fixed Effects ; Generalized F test ; Generalized p-value ; Inter-laboratory studies ; Random Effects ; Welch test
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Abstract:
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This article is about testing the equality of several normal means when the variances are unknown and arbitrary. Even though several tests are available in the literature, none of them perform well in terms of type I error probability under various sample size and parameter combinations. We propose a parametric bootstrap (PB) approach and compare it with three existing location-scale invariant tests: the Welch test, James test, and the generalized F test. The size and power properties of the tests are evaluated using Monte Carlo simulation. Our studies show that the PB test is the best among the four tests with respect to size properties. It is also noted that the same tests can be used to test the significance of the random effect variance component in a one-way random model under unequal error variances. Such models are widely used to analyze data from inter-laboratory studies.
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