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.
`...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.
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
Series: Oxford Statistical Science Series
Number Of Pages: 204
Published: 1st October 1997
Publisher: Oxford University Press
Country of Publication: GB
Dimensions (cm): 24.2 x 16.1
Weight (kg): 0.44