An efficient cross-validation algorithm for window width selection for nonparametric kernel regression

1 January 1993

journal article
research article
Published by Taylor & Francis in Communications in Statistics - Simulation and Computation

Vol. 22 (4) , 1107-1114
https://doi.org/10.1080/03610919308813144

Abstract

This paper presents an approach to cross-validated window width choice which greatly reduces computation time, which can be used regardless of the nature of the kernel function, and which avoids the use of the Fast Fourier Transform. This approach is developed for window width selection in the context of kernel estimation of an unknown conditional mean.

Keywords

This publication has 4 references indexed in Scilit:

Recent Developments in Nonparametric Density Estimation
Journal of the American Statistical Association, 1991
Applied Nonparametric Regression
Published by Cambridge University Press (CUP) ,1990
On Non-Parametric Estimates of Density Functions and Regression Curves
Theory of Probability and Its Applications, 1965
Density Estimation for Statistics and Data Analysis
Published by Springer Nature ,1400