K-nn Nonparametric Estimation Of Regression Functions In the Presence of Irrelevant Variables
نویسندگان
چکیده
We show that when estimating a nonparametric regression model, the knearest-neighbor nonparametric 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 nonparametric series (spline) estimator when there exist irrelevant regressors.
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