Applied Nonparametric Regression
Series: Econometric Society Monographs; 19;
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Product details:
- Edition number New ed
- Publisher Cambridge University Press
- Date of Publication 31 January 1992
- ISBN 9780521429504
- Binding Paperback
- No. of pages352 pages
- Size 229x152x20 mm
- Weight 520 g
- Language English 0
Categories
Short description:
This is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable.
MoreLong description:
Applied Nonparametric Regression is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable. The computer and the development of interactive graphics programs have made curve estimation possible. 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. H&&&228;rdle argues that all smoothing methods are based on a local averaging mechanism and can be seen as essentially equivalent to kernel smoothing. To simplify the exposition, kernel smoothers are introduced and discussed in great detail. Building on this exposition, various other smoothing methods (among them splines and orthogonal polynomials) are presented and their merits discussed. All the methods presented can be understood on an intuitive level; however, exercises and supplemental materials are provided for those readers desiring a deeper understanding of the techniques. The methods covered in this text have numerous applications in many areas using statistical analysis. Examples are drawn from economics as well as from other disciplines including medicine and engineering.
"Professor H&&&228;rdle 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
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.
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