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On internally corrected and symmetrized kernel estimators for nonparametric regression

Linton, Oliver and Jacho-Chávez, David (2010) On internally corrected and symmetrized kernel estimators for nonparametric regression. TEST, 19 (1). pp. 166-186. ISSN 1133-0686

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Abstract

We investigate the properties of a kernel-type multivariate regression estimator first proposed by Mack and Müller (Sankhya 51:59–72, 1989) in the context of univariate derivative estimation. Our proposed procedure, unlike theirs, assumes that bandwidths of the same order are used throughout; this gives more realistic asymptotics for the estimation of the function itself but makes the asymptotic distribution more complicated. We also propose a modification of this estimator that has a symmetric smoother matrix, which makes it admissible, unlike some other common regression estimators. We compare the performance of the estimators in a Monte Carlo experiment. Multivariate regression - Smoothing matrix - Symmetry

Item Type: Article
Official URL: http://www.springer.com/statistics/journal/11749
Additional Information: © 2010 Springer
Library of Congress subject classification: Q Science > QA Mathematics
Sets: Research centres and groups > Financial Markets Group (FMG)
Research centres and groups > Suntory and Toyota International Centres for Economics and Related Disciplines (STICERD)
Departments > Economics
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Identification Number: UT ISI:000276227700016
Date Deposited: 16 Jul 2010 14:29
URL: http://eprints.lse.ac.uk/28619/

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