Introduction to Nonparametric Regression (eBook)

K. Takezawa (Autor)

eBook Download: PDF

2005 | 1. Auflage
640 Seiten
John Wiley & Sons (Verlag)
978-0-471-77144-9 (ISBN)

Infos

An easy-to-grasp introduction to nonparametric regression

This book's straightforward, step-by-step approach provides an
excellent introduction to the field for novices of nonparametric
regression. Introduction to Nonparametric Regression clearly
explains the basic concepts underlying nonparametric regression and
features:

* Thorough explanations of various techniques, which avoid complex
mathematics and excessive abstract theory to help readers
intuitively grasp the value of nonparametric regression
methods

* Statistical techniques accompanied by clear numerical examples
that further assist readers in developing and implementing their
own solutions

* Mathematical equations that are accompanied by a clear
explanation of how the equation was derived

The first chapter leads with a compelling argument for studying
nonparametric regression and sets the stage for more advanced
discussions. In addition to covering standard topics, such as
kernel and spline methods, the book provides in-depth coverage of
the smoothing of histograms, a topic generally not covered in
comparable texts.

With a learning-by-doing approach, each topical chapter includes
thorough S-Plus? examples that allow readers to duplicate the same
results described in the chapter. A separate appendix is devoted to
the conversion of S-Plus objects to R objects. In addition, each
chapter ends with a set of problems that test readers' grasp of key
concepts and techniques and also prepares them for more advanced
topics.

This book is recommended as a textbook for undergraduate and
graduate courses in nonparametric regression. Only a basic
knowledge of linear algebra and statistics is required. In
addition, this is an excellent resource for researchers and
engineers in such fields as pattern recognition, speech
understanding, and data mining. Practitioners who rely on
nonparametric regression for analyzing data in the physical,
biological, and social sciences, as well as in finance and
economics, will find this an unparalleled resource.

Autor

KUNIO TAKEZAWA, PhD, is a Specific Research Scientist in the Department of Information Science and Technology at the National Agricultural Research Center, Japan. He is also an Associate Professor in the Cooperative Graduate School System at the Graduate School of Life and Environmental Sciences at the University of Tsukuba, Japan. Dr. Takezawa holds several patents in mathematics and is the recipient of a Research Award from the Japan Science and Technology Agency and a Thesis Award from the Japanese Agricultural Systems Society.

Inhalt

Preface.

Acknowledgments.

1. Exordium.

2. Smoothing for Data with an Equispaced Predictor.

3. Nonparametric Regression for One-Dimensional Predictor.

4. Multidimensional Smoothing.

5. Nonparametric Regression with Predictors Represented as
Distributions.

6. Smoothing of Histograms and Nonparametric Probability Density
Functions.

7. Pattern Recognition.

Appendix A: Creation and Applications of B-Spline Bases.

Appendix B: R Objects.

Appendix C: Further Readings.

Index.

Rezensionen

"...provides an accessible theoretical treatment of
nonparametric regression." (Journal of the American Statistical
Association, December 2006)

"...I like this book, and recommend it to graduate students
and researchers who plan to implement nonparametric models in their
research." (Technometrics, November 2006)

"A very useful book clearly presenting basic concepts of
nonparametric regression and applications to various real-life
situations...highly recommended." (CHOICE, June
2006)

"...a practical introduction to nonparametric
regression..." (Journal of Quality Technology, April
2006)

"...the presentation is very lucid and
easy-to-follow...this book will highly be appreciated by
students." (MAA Reviews, March 14, 2006)

"...deals concisely with the application of non-parametric
regression to multidimensional data..." (Journal of
Applied Statistics, 2007)

Produkt-Details

• Erscheint lt. Verlag: 13.12.2005
• Reihe/Serie: Wiley Series in Probability and Statistics
• Reihe/Serie: Wiley Series in Probability and Statistics
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Technik
• Schlagworte: nichtparametrische Verfahren • Nonparametric Analysis • Regression Analysis • Regressionsanalyse • Statistics • Statistik
• ISBN-10: 0-471-77144-9 / 0471771449
• ISBN-13: 978-0-471-77144-9 / 9780471771449