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

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 emphasized, 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.

General

Imprint:	Oxford UniversityPress
Country of origin:	United Kingdom
Series:	Oxford Statistical Science Series, 18
Release date:	August 1997
First published:	August 1997
Authors:	Adrian W. Bowman (Professor of Statistics) • Adelchi Azzalini (Professor of Statistics)
Dimensions:	242 x 161 x 15mm (L x W x T)
Format:	Hardcover
Pages:	204
ISBN-13:	978-0-19-852396-3
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Academic & Education > Varsity Textbooks > Computer Science Promotions Books > Academic & Education > UNISA > Computer Science
LSN:	0-19-852396-3
Barcode:	9780198523963
Course Code:	STA4809

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