JSM 2005 Online Program

Abstract #302892

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Activity Number:	229
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Nonparametric Statistics
Abstract - #302892
Title:	Minimum Distance Errors in Variables Regression Model Fitting
Author(s):	Weixing Song*+
Companies:	Michigan State University
Address:	1205F University Village, East Lansing, MI, 48823, United States
Keywords:	Minimum Distance ; Ordinary Smoothing ; Deconvolution Kernel Estimator ; Nonparametric Regression
Abstract:	This paper discusses a class of minimum distance tests for fitting a parametric regression model to a regression function in the errors in variables model with known design distribution and ordinary smooth measurement error. These tests are based on certain minimized L2 distances between a nonparametric regression function estimator and the parametric model being fitted. The estimator of the nonparametric regression function is constructed by using deconvolution method. The paper establishes the asymptotic normality of the proposed test statistics under the null hypothesis and some asymptotic results of the corresponding minimum distance estimators based on the value of asymptotic variance. Some simulation studies are conducted, which shows 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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