Data sharpening methods in multivariate local quadratic regression

JOURNAL OF MULTIVARIATE ANALYSIS

卷:105

Article

Wang, Xiaoying^2,3  Jiang, Song¹  Yin, Junping¹ 

[1] Inst Appl Phys & Computat Math, Beijing 100088, Peoples R China

[2] N China Elect Power Univ, Sch Math & Phys, Beijing 102206, Peoples R China

[3] Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing 100190, Peoples R China

关键词: Data sharpening methods;    Multivariate nonparametric regression;    Local quadratic estimator;    Asymptotic conditional bias and variance;    Fitting precision;    Bandwidth matrix;   

DOI  :  10.1016/j.jmva.2011.09.004

来源: Elsevier

【 摘 要 】

This paper is concerned with the conditional bias and variance of local quadratic regression to the multivariate predictor variables. Data sharpening methods of nonparametric regression were first proposed by Choi, Hall, Roussion. Recently, a data sharpening estimator of local linear regression was discussed by Naito and Yoshizaki. In this paper, to improve mainly the fitting precision, we extend their results on the asymptotic bias and variance. Using the data sharpening estimator of multivariate local quadratic regression, we are able to derive higher fitting precision. In particular, our approach is simple to implement, since it has an explicit form, and is convenient when analyzing the asymptotic conditional bias and variance of the estimator at the interior and boundary points of the support of the density function. (C) 2011 Elsevier Inc. All rights reserved.

【 授权许可】

Free