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- W4231576688 abstract "This chapter explores the relationship of the underlying density estimator to the regression estimator. Many regression estimators discussed in the chapter are linear smoothers, that is, linear combinations of the observed responses. Issues of robustness are relevant, particularly in higher dimensions. The chapter describes nonlinear nonparametric smoothers. A wide range of nonparametric regression algorithms, including LOWESS and smoothing splines, have been shown to be linear smoothers. To compare these estimates to the original kernel estimator, it is natural to compare the weight vectors multiplying the response vector. The Nadaraya–Watson kernel weights are shown in the chapter for the biweight kernel with a bandwidth, which provides a smoother estimator than the local polynomial fit." @default.
- W4231576688 created "2022-05-12" @default.
- W4231576688 date "2015-03-13" @default.
- W4231576688 modified "2023-10-16" @default.
- W4231576688 title "Nonparametric Regression and Additive Models" @default.
- W4231576688 doi "https://doi.org/10.1002/9781118575574.ch8" @default.
- W4231576688 hasPublicationYear "2015" @default.
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