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Activity Number:
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257
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Type:
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Invited
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Date/Time:
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Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #307812 |
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Title:
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Robust Tests in Regression Models with Omnibus Alternatives and Bounded Influence
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Author(s):
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Lan Wang*+
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Companies:
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The University of Minnesota
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Address:
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385 Ford Hall, School of Statistics, Minneapolis, MN, 55455,
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Keywords:
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robust tests ; conditional moment test ; influence function ; lack-of-fit tests ; smooth alternative ; regression models
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Abstract:
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A robust approach for testing the parametric form of a regression function versus an omnibus alternative is introduced. This generalizes existing robust methods for testing subhypotheses in a regression model. The new test is motivated by developments in modern smoothing-based testing procedures and can be viewed as a robustification of a smoothing-based conditional moment test. It is asymptotically normal under both the null hypothesis and local alternatives. The robustified test retains the "omnibus" property of the corresponding smoothing test, i.e., it is consistent for any fixed smooth alternative in an infinite dimensional space. It is shown that the bias of the asymptotic level under shrinking local contamination is bounded only if the second-order Hampel's influence function is bounded.
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