On Internally Corrected and Symmetrized Kernel Estimators for Nonparametric Regression Articles uri icon

authors

  • LINTON ., OLIVER
  • JACHO-CHAVEZ, DAVID T.

published in

publication date

  • May 2010

start page

  • 166

end page

  • 186

issue

  • 1

volume

  • 19

International Standard Serial Number (ISSN)

  • 1133-0686

Electronic International Standard Serial Number (EISSN)

  • 1863-8260

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.