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Activity Number:
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110
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 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 - #308813 |
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Title:
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Model-Checking in Errors-in-Variables Regression
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Author(s):
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Weixing Song*+
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Companies:
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Kansas State University
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Address:
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Department of Statistics, Manhattan, KS, 66502,
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Keywords:
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Errors-in-Variables Model ; Deconvolution Kernel ; Minimum Distance ; Lack-of-Fit
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Abstract:
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This paper discusses a class of minimum distance tests for fitting a parametric regression model to a class of regression function in the errors-in-variables model. These tests are based on certain minimized distances between a nonparametric regression function estimator and a deconvolution kernel estimator of the conditional expectation of the parametric model being fitted. The paper establishes the asymptotic normality of the proposed test statistics under the null hypothesis and that of the corresponding minimum distance estimators. The significant contribution made in this research is the removal of the common assumption in the literature that the density function of the design variable is known. A simulation study shows that the testing procedures are quite satisfactory in the preservation of the finite sample level and in terms of a power comparison.
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