Applied Smoothing Techniques for Data Analysis

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...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.

Scienctific Computing World 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.

N. Veraverbeke, Short Book Reviews, August 1998 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.

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.

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

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