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Activity Number:
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508
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Type:
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Contributed
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Date/Time:
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Thursday, August 10, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #307400 |
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Title:
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Inference in a Simple Random Effects Model with Low Replication and Nonnormal Distributions
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Author(s):
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Hongjuan Liu*+ and Xinping Cui
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Companies:
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University of California, Riverside and University of California, Riverside
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Address:
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Department of Statistics, 3420 Kentucky Street, Riverside, CA, 92507,
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Keywords:
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components of variance ; nonparametric
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Abstract:
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We studied the nonparametric inference on the group effect in a random effect components of variance model when the number of groups diverges without bound, but the replications remain fixed as small as 2.With normality assumption, the exact F-test can infer the existence of group effect. When normality assumption is not valid, it has been shown that the F-statistic is robust only in a balanced design and the asymptotic distribution of the F-statistic requires the existence of the fourth order moment. In this work, we pushed the frontier to a very general setting. Our new method only requires the existence of the second order moment and can be applied to balanced or unbalanced data under skewed or heavy-tailed distribution. Our simulations show that the proposed method is very powerful and computationally efficient. We also show its application in microarray-based heritability analysis.
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