Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-PLUS Illustrations

TLDR

Nonparametric and semiparametric techniques such as kernel density estimation and regression are widely used for exploring and modeling data, including time series, and for comparing regression curves and surfaces. The paper develops and illustrates kernel‑based smoothing methods for inference in density estimation and nonparametric regression. 1.

Abstract

1. Density estimation for exploring data 2. Density estimation for inference 3. Nonparametric regression for exploring data 4. Inference with nonparametric regression 5. Checking parametric regression models 6. Comparing regression curves and surfaces 7. Time series data 8. An introduction to semiparametric and additive models References