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.
Rezensionen / Stimmen
...a well-written book that fills an obvious gap in the statistics literature...a pragmatic introduction to the application of smoothing methods. The book's layout and structure are well designed and its language lucid. Examples are drawn from a range of disciplines and should appeal to a broad readership...Statisticians, who are familiar with applied non-parametric smoothing through programmed uncertainty estimates may want to check this book anyway for the odd trick they may have missed. For anyone who lacks one or more of those elements, and is involved in any way with data analysis, it is an excellent buy. * Scientific Computing World, April 1998 * A well-written book that fills an obvious gap in the statistics literature.....a pragmatic introduction to the application of smoothing methods. The book's layout and structure are well designed and its language lucid. Examples are drawn from a range of disciplines and should appeal to a broad readership.....an excellent buy. * Scienctific Computing World * This must be a very attractive book: when it was lying on my desk while preparing this review, it constantly taken away by students and colleagues who were attracted by the topic and the nice presentation with graphics, examples, S-Plus material, etc....A glance at the more than two-hundred references reveals that most of them date from the nineties and hence it becomes clear that this is an up-to-date book with the most recent state of the art. * N. Veraverbeke, Short Book Reviews, August 1998 * There is a rich choice of examples, exercises, hints for further reading and S-Plus illustrations. Compared to the several other recent books in the area, the present monograph has the advantage of being introductory and practcial within a very reasonable number of pages.
Reihe
Sprache
Verlagsort
Zielgruppe
Produkt-Hinweis
Fadenheftung
Gewebe-Einband
Illustrationen
Maße
Höhe: 241 mm
Breite: 164 mm
Dicke: 22 mm
Gewicht
ISBN-13
978-0-19-852396-3 (9780198523963)
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Schweitzer Klassifikation
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
Autor*in
Professor of StatisticsProfessor of Statistics, University of Glasgow, Scotland
Professor of StatisticsProfessor of Statistics, University of Padua, Italy
1. Density estimation for exploring data ; 2. Density estimation for inference ; 3. Nonparametric regression for exploring data ; 4. Inference with nonparametric regression ; 5. Checking parametric regression models ; 6. Comparing regression curves and surfaces ; 7. Time series data ; 8. An introduction to semiparametric and additive models ; References
