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

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 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 the book. Examples are drawn from 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 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.Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations is written by Adrian W. Bowman; Adelchi Azzalini and published by OUP Oxford. ISBNs for Applied Smoothing Techniques for Data Analysis are 9780191545696, 0191545694 and the print ISBNs are 9780198523963, 0198523963.