A simple bootstrap bandwidth selector for local polynomial fitting. (English) Zbl 1178.62036
Summary: A new, fully data-driven bandwidth selector with a double smoothing (DS) bias term and a data-driven variance estimator is developed following the bootstrap idea. The data-driven variance estimation does not involve any additional bandwidth selection. The proposed bandwidth selector convergences faster than a plug-in one due to the DS bias estimate, whereas the data-driven variance improves its finite sample performance clearly and makes it stable. Asymptotic results of the proposals are obtained. A comparative simulation study was done to show the overall gains and the gains obtained by improving either the bias term or the variance estimate, respectively. It is shown that the use of a good variance estimator is more important when the sample size is relatively small.
MSC:
62G08 | Nonparametric regression and quantile regression |
62G09 | Nonparametric statistical resampling methods |
62G20 | Asymptotic properties of nonparametric inference |
65C60 | Computational problems in statistics (MSC2010) |