Cross-validation and non-parametric k nearest-neighbour estimation Academic Article

abstract

• In this paper we consider the problem of estimating a non-parametric regression function using the k nearest-neighbour method. We provide asymptotic theories for the least-squares cross validation (CV) selected smoothing parameter k for both local constant and local linear estimation methods. We also establish the asymptotic normality results for the resulting non-parametric regression function estimators. Some limited Monte Carlo experiments show that the CV method performs well in finite sample applications. Royal Economic Society 2006.

authors

• Li, Qi

published proceedings

• ECONOMETRICS JOURNAL

author list (cited authors)

• Ouyang, D., Li, D., & Li, Q. i.

citation count

• 13

complete list of authors

• Ouyang, Desheng||Li, Dong||Li, Qi

publication date

• November 2006

publisher

• Oxford University Press (OUP)  Publisher

published in

• Econometrics Journal  Journal

keywords

• Asymptotic Normality
• Cross Validation
• K Nearest Neighbour
• Nonparametric Smoothing

Digital Object Identifier (DOI)

• 10.1111/j.1368-423X.2006.00193.x

start page

• 448

end page

• 471

volume

• 9

issue

• 3

URL

• http://dx.doi.org/10.1111/j.1368-423x.2006.00193.x