Applied Smoothing Techniques for Data Analysis

The Kernel Approach with S-Plus Illustrations

Autor: Adelchi Azzalini, A.W. Bowman

Jazyk:

Väzba: Pevná

Vydavateľ: Oxford University Press

Dostupnosť: Skladom u dodávateľa

Odosielame za 10-18 dní

143.00 €

The book describes the use of smoothing techniques in statistics, including both density estimation...

Informácie o knihe

Autor

Adelchi Azzalini, A.W. Bowman

Jazyk

Angličtina

Väzba

Kniha - Pevná

Vydalo

1997

Stránok

204

EAN

9780198523963

ISBN

0198523963

Enbook ID

04527199

Vydavateľ

Oxford University Press

Hmotnosť

444

Rozmery

161 x 241 x 19

Kompletný popis

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.

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Applied Smoothing Techniques for Data Analysis

The Kernel Approach with S-Plus Illustrations

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Kompletný popis

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