Cross-validation and non-parametric k nearest-neighbour estimation
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
Volume (Year): 9 (2006)
Issue (Month): 3 (November)
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