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Activity Number:
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30
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Type:
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Contributed
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Date/Time:
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Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #305061 |
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Title:
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Minimum Distance Conditional Variance Function Checking
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Author(s):
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Weixing Song*+ and Nishantha Samarakoon
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Companies:
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Kansas State University and Kansas State University
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Address:
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, , ,
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Keywords:
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Kernel estimator ; Lack-of-fit test ; Heteroscedasticity ; Variance function ; L2 distance
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Abstract:
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We will discuss a class of minimum distance tests for fitting a parametric variance function in heteroscedastic regression models. These tests are based on certain minimized L2 distances between a nonparametric variance function estimator and the parametric variance function estimator being fitted. This work establishes the asymptotic normality of the proposed test statistics and that of the corresponding minimum distance estimator under the fitted model. These estimators turn out to be square root n consistent. Consistency of this test at some fixed alternatives and asymptotic power under some local nonparametric alternatives are also discussed. Some simulation studies are conducted to assess the finite sample performance of the proposed test.
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