Cross-validation and non-parametric k nearest-neighbour estimation
Dong Li () and
Econometrics Journal, 2006, vol. 9, issue 3, pages 448-471
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. Copyright Royal Economic Society 2006
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Persistent link: http://EconPapers.repec.org/RePEc:ect:emjrnl:v:9:y:2006:i:3:p:448-471
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