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Applied Nonparametric Regression
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Details

  • Page extent: 352 pages
  • Size: 228 x 152 mm
  • Weight: 0.52 kg

Paperback

 (ISBN-13: 9780521429504 | ISBN-10: 0521429501)

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.

• 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

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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