ABSTRACT
In many applications, we might make distributional and model assumptions about the underlying process that generated the data, so we can use a parametric approach in our analysis. The parametric approach offers many benefits (known sampling distributions, lower computational burden, etc.), but can be dangerous if the assumed model is incorrect. At the other end of the data-analytic spectrum, we have the nonparametric approach, where one does not make any formal assumptions about the underlying structure or process. When our goal is to summarize the relationship between variables, then smoothing methods are a bridge between these two approaches. Smoothing methods make the weak assumption that the relationship between variables can be represented by a smooth curve or surface. In this chapter, we cover the loess method for scatterplot smoothing and its various extensions. Spline smoothing is briefly discussed at the end in conjunction with the Curve Fitting Toolbox from The MathWorks.
