Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations (Oxford Statistical Science Series)

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9780198523963: Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations (Oxford Statistical Science Series)

This book describes the use of smoothing techniques in statistics and includes both density estimation and nonparametric regression. Incorporating recent advances, it describes a variety of ways to apply these methods to practical problems. Although the emphasis is on using smoothing techniques to explore data graphically, the discussion also covers data analysis with nonparametric curves, as an extension of more standard parametric models. Intended as an introduction, with a focus on applications rather than on detailed theory, the book will be equally valuable for undergraduate and graduate students in statistics and for a wide range of scientists interested in statistical techniques.

The text makes extensive reference to S-Plus, a powerful computing environment for exploring data, and provides many S-Plus functions and example scripts. This material, however, is independent of the main body of text and may 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

Review:


"An up-to-date book with the most recent state of the art. . . . Accessible to nonmathematical readers. . .There is a rich choice of examples, exercises, hints for further reading and S-Plus illustrations." --N. Veraverbeke, Limburgs Universitair Centrum, Diepenbeek, Belgium


"[T]his book provides an overview of smoothing techniques used in data analysis, with emphasis on one- and two-dimensional data. The authors' aim is to complement the existing books by focusing on intuitive presentation of the ideas and on practical issues of inference rather than estimation. The book consists of eight chapters and 193 pages, with the first two chapters devoted to density estimation and the last six . . . concentrating on smoothing in regression and time series. Real data are used throughout to illustrate the techniques. . . . [T]he book attempts to be both a practical introduction to smoothing and an outline of the methodological and theoretical development of the subject. It does reasonably well at both, but its strength is in showing the techniques and illustrating them on datasets. I think it will be a quite useful book for a research or applied statistician wanting an overview of the subject with examples and references."--Technometrics


"This instructive textbook provides an excellent introduction to smoothing, with an emphasis on methods, applications on real data, and subsequent inferences. If you are an applied and/or a quantitatively oriented researcher who is unfamiliar with (or suspicious of) smoothing methods, you will definitely appreciate the book's level and practical focus, as the authors have presented the methodology and have demonstrated implementation clearly on real datasets with descriptive interpretations of the results. . . . This book would serve as an excellent textbook for a masters-level course on smoothing because it focuses on actual practice, through real datasets and corresponding software (available on-line as described in Appendix A) and because of the instructive exercises that conclude each chapter."--Journal of the American Statistical Association


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Descripción Oxford University Press, United Kingdom, 1997. Hardback. Estado de conservación: New. Language: English . Brand New Book ***** Print on Demand *****. 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. Nº de ref. de la librería APC9780198523963

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Descripción Oxford University Press, United Kingdom, 1997. Hardback. Estado de conservación: New. Language: English . Brand New Book ***** Print on Demand *****.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. Nº de ref. de la librería APC9780198523963

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Descripción Oxford University Press, 2017. Hardcover. Estado de conservación: New. This item is printed on demand. Nº de ref. de la librería 0198523963

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