K-nearest-neighbour non-parametric estimation of regression functions in the presence of irrelevant variables
We show that when estimating a non-parametric regression model, the k-nearest-neighbour non-parametric estimation method has the ability to remove irrelevant variables provided one uses a product weight function with a vector of smoothing parameters, and the least-squares cross-validation method is used to select the smoothing parameters. Simulation results are consistent with our theoretical analysis and show that the performance of the k-nn estimator is comparable to the popular kernel estimator; and it dominates a non-parametric series (spline) estimator when there exist irrelevant regressors. Copyright © 2008 The Author(s). Journal compilation © Royal Economic Society 2008
Volume (Year): 11 (2008)
Issue (Month): 2 (July)
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