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Zusatztext 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. Informationen zum Autor 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 Klappentext 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. Zusammenfassung Describes the use of smoothing techniques in statistics, with an emphasis on applications. This book also makes extensive reference to S-Plus, as a computing environment in which examples can be explored. It provides S-Plus functions and example scripts to implement many of the techniques described. Inhaltsverzeichnis 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 ...
