Applied Nonparametric Regression

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Cambridge University Press, 1990 - 333 Seiten
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. Härdle 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.
 

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Inhalt

Introduction
3
Basic idea of smoothing
14
Smoothing techniques
24
How close is the smooth to the true curve?
89
Choosing the smoothing parameter
147
Data sets with outliers
190
Nonparametric regression techniques
203
Looking for special features and qualitative
217
Incorporating parametric components
232
Investigating multiple regression by additive
257
A desirable computing environment
291
References
305
Name inder
325
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