Synopsis
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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About the Author
Professor Adrian Bowman, Department of Statistics, University of Glasgow, Glasgow, G12 8QQ, Scotland, U.K. Tel: 0141-330- 4046, Fax: 0141-330-4814, E-mail: adrian@stats.gla.ac.uk Professor Adelchi Azzalini, Department of Statistical Sciences, University of Padova, Via S.Francesco 33, 35121 Padova, Italy Tel:0039-49-8274147, Fax: 0039-49-8753930, E-mail: adelchi@pearson.stat.unipd.it
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Drug Treatment of Hyperlipidemia [Hardcover] [Aug 14, 1997] Adrian W Bowman and Adelchi Azzalini
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Applied Smoothing Techniques for Data Analysis
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Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations (Oxford Statistical Science Series)
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Applied Smoothing Techniques for Data Analysis (Hardcover)
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ISBN 10: 0198523963
ISBN 13: 9780198523963
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Hardcover. Condition: new. 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 emphasised, but the use of nonparametric curves in drawingconclusions 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 seekan 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-Plusfunctions 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 byreaders not interested in S-Plus. This text for statisticians and those who wish to apply smoothing techniques includes coverage of: density estimation for exploring data; density estimation for inference; nonparametric regression for exploring data; and comparing regression curves and surfaces. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9780198523963
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Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations (Oxford Statistical Science Series)
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