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, Volume 19, Issue 1, pp 166–186 | Cite as

On internally corrected and symmetrized kernel estimators for nonparametric regression

  • Oliver B. LintonEmail author
  • David T. Jacho-Chávez
Original Paper

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.

Keywords

Multivariate regression Smoothing matrix Symmetry 

Mathematics Subject Classification (2000)

62G08 62G20 

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Copyright information

© Sociedad de Estadística e Investigación Operativa 2009

Authors and Affiliations

  1. 1.Department of EconomicsLondon School of EconomicsLondonUK
  2. 2.Department of EconomicsIndiana UniversityBloomingtonUSA

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