Nonparametric smoothing methods serve several needs in statistical data analysis: They provide a flexible analysis tool, often based on interactive graphical data representation. Also, they help in constructing a model from observations, for example by graphical comparison with already existing models.
This chapter focuses on function estimation by kernel smoothing methods. Of course, we cannot give a profound introduction into this topic. We refer therefore to textbooks that provide general introductions to smoothing techniques: Silverman (1986), Härdle (1990) and Scott (1992) for nonparametric density estimation, Härdle (1991), Wand and Jones (1995) for nonparametric regression.
Before proceeding to the next section, please type
library("smoother") library("plot")to load the necessary libraries. The smoother library automatically loads the xplore and the kernel library, which we will use as well. Additionally, we load the library plot which is used for graphing the resulting density and regression functions.