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

About this book

Applied Smoothing Techniques for Data Analysis 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 be applied 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 Applied Smoothing Techniques for Data Analysis. Examples are drawn from a wide range of applications. Applied Smoothing Techniques for Data Analysis 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 Applied Smoothing Techniques for Data Analysis 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 scripts are 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.

Contents

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

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