Applied nonparametric regression

By: Hardle,WolfgangMaterial type: TextTextSeries: Econometric Society monographs, no. 19Publication details: Cambridge, U.K. : Cambridge University Press, 1991Description: xv, 333 pages : illustrationsISBN: 9780521429504; 0521429501 ; 9780521382489; 0521382483 Subject(s): Regression analysisDDC classification: 519.536
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.
Summary: 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.
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Includes Index

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.

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.

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