APPLIED SMOOTHING TECHNIQUES FOR DATA ANALYSIS. THE KERNEL APPROACH WITH S-PLUS ILLUSTRATIONS

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

The book describes the use of smoothing techniques in statistics, including both density estimation and nonparametric regression. Considerable advances in research in this area have been made in recent years. The aim of this text is to describe a variety of ways in which these methods can beapplied to practical problems in statistics. The role of smoothing techniques in exploring data graphically is emphasised, but the use of nonparametric curves in drawing conclusions from data, as an extension of more standard parametric models, is also a major focus of the book. Examples are drawnfrom a wide range of applications. The book is intended for those who seek an introduction to the area, with an emphasis on applications rather than on detailed theory. It is therefore expected that the book will benefit those attending courses at an advanced undergraduate, or postgraduate, level,as well as researchers, both from statistics and from other disciplines, who wish to learn about and apply these techniques in practical data analysis. The text makes extensive reference to S-Plus, as a computing environment in which examples can be explored. S-Plus functions and example scriptsare provided to implement many of the techniques described. These parts are, however, clearly separate from the main body of text, and can therefore easily be skipped by readers not interested in S-Plus.