Applied Nonparametric Regression
Applied Nonparametric Regression
CAD$53.95
Applied Nonparametric Regression brings together in one place the techniques for regression curve smoothing involving more than one variable. The computer and the development of interactive graphics programs has made curve estimation popular. This volume focuses on the applications and practical problems of two central aspects of curve smoothing: the choice of smoothing parameters and the construction of confidence bounds. The methods covered in this text have numerous applications in many areas using statistical analysis. Examples are drawn from economics--such as the estimation of Engel curves--as well as other disciplines including medicine and engineering. For practical applications of these methods a computing environment for exploratory Regression--XploRe--is described.
- First book to bring together curve estimation techniques
- Numerous applications for statistical analysis
- Includes exercises and examples drawn from practical applications, e.g. medicine and engineering
Reviews & endorsements
"Professor Härdle has provided us with an important book, one that will be appreciated both by applied statisticians who want to implement nonparametric regression techniques and by theoreticians interested in becoming knowledgeable in this growing field. Applied Nonparametric Regression is a very welcome addition to the literature." Journal of the American Statistical Association
"Nonparametric regression analysis has become central to economic theory. Härdle, by writing the first comprehensive and accessible book on the subject, has contributed enormously to making nonparametric regression equally central to econometric practice." Charles F. Manski, University of Wisconsin, Madison
"This book represents an optimally estimated common thread for the numerous topics and results in the fast-growing area of nonparametric regression. The user-friendly approach taken by the author has successfully smoothed out most of the formidable asymptotic elaboration in developing the theory. This is an excellent collection for both beginners and experts." Ker-Chau Li, University of California, Los Angeles
"This monograph on nonparametric regression presents a particularly clear and balanced view of the methodology and practice of this very important subject, and so is of use to theoreticians and practitioners alike." Peter Hall, University of Glasgow
"This book makes the main ideas and methodologies of nonparametric regression easily accessible to nonexperts, and is a valuable reference source for experts as well because of its wide scope." J.S. Marron
"...Härdle has written an important book on NPR that will undoubtedly serve as one of the standards in this field for some time to come." R. L. Eubank, Technometrics
Product details
January 1992Paperback
9780521429504
352 pages
229 × 152 × 20 mm
0.52kg
Available
Table of Contents
- Preface
- Part I. Regression Smoothing:
- 1. Introduction
- 2. Basic idea of smoothing 3. Smoothing techniques
- Part II. The Kernel Method:
- 4. How close is the smooth to the true curve?
- 5. Choosing the smoothing parameter
- 6. Data sets with outliers
- 7. Smoothing with correlated data
- 8. Looking for special features (qualitative smoothing)
- 9. Incorporating parametric components and alternatives
- Part III. Smoothing in High Dimensions:
- 10. Investigating multiple regression by additive models
- Appendices
- References
- List of symbols and notation.
