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Abstract Details

Activity Number: 245
Type: Contributed
Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #308352
Title: Variance Function Estimator in the Multiple Regression Model
Author(s): Kee-Hoon Kang*+ and Seokoh Jeong
Companies: Hankuk University of Foreign Studies and Hankuk University of Foreign Studies
Address: Mohyun-myeon, Yongin, International, 449-791, Korea (South)
Keywords: Difference based estimator ; Nonparametric regression
Abstract:

Variance functions in regression models are quite important as pointed out by Carroll (1982) and Hall and Carroll (1989). Recently, semi- and nonparametric methods for estimating variance function in heteroscedastic regression models take much interest in the related research. In this paper, a difference-based variance function estimator in the nonparametric regression model with multiple predictors is proposed, which extends the approach by Lee, Kim and Park (2006) to the heteroscedastic regression model. We obtain the asymptotic bias and variance of the proposed estimator and explore the numerical performance via simulation.


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