Statistical Theory and Computational Aspects of Smoothing

1. Front Matter

   Pages I-VIII

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2. A Personal View of Smoothing and Statistics

   • J. S. Marron

   Pages 1-9

3. Smoothing by Local Regression: Principles and Methods

   • William S. Cleveland, Clive Loader

   Pages 10-49

4. Variance Properties of Local Polynomials and Ensuing Modifications

   • Burkhardt Seifert, Theo Gasser

   Pages 50-79

5. Comments

   • Peter Hall, Berwin A. Turlach

   Pages 80-84

6. Comments

   • M. C. Jones

   Pages 85-87

7. Comments

   • C. Thomas-Agnan

   Pages 88-92

8. Comments

   • S. J. Sheather, M. P. Wand, M. S. Smith, R. Kohn

   Pages 93-102

9. Rejoinder

   • J. S. Marron

   Pages 103-112

10. Rejoinder

    • William S. Cleveland, Clive Loader

    Pages 113-120

11. Rejoinder

    • Burkhardt Seifert, Theo Gasser

    Pages 121-127

12. Robust Bayesian Nonparametric Regression

    • C. K. Carter, R. Kohn

    Pages 128-148

13. The Invariance of Statistical Analyses with Smoothing Splines with Respect to the Inner Product in the Reproducing Kernel Hilbert Space

    • Angelika van der Linde

    Pages 149-164

14. A Note on Cross-Validation for Smoothing Splines

    • Gerhard P. Neubauer, Michael G. Schimek

    Pages 165-177

15. Some Comments on Cross-Validation

    • Bernd Droge

    Pages 178-199

16. Extreme Percentile Regression

    • Ori Rosen, Ayala Cohen

    Pages 200-214

17. Mean and Dispersion Additive Models

    • Robert A. Rigby, Mikis D. Stasinopoulos

    Pages 215-230

18. Interaction in Nonlinear Principal Components Analysis

    • G. D. Costanzo, J. L. A. van Rijckevorsel

    Pages 231-246

19. Nonparametric Estimation of Additive Separable Regression Models

    • R. Chen, W. Härdle, O. B. Linton, E. Severance-Lossin

    Pages 247-265

One of the main applications of statistical smoothing techniques is nonparametric regression. For the last 15 years there has been a strong theoretical interest in the development of such techniques. Related algorithmic concepts have been a main concern in computational statistics. Smoothing techniques in regression as well as other statistical methods are increasingly applied in biosciences and economics. But they are also relevant for medical and psychological research. Introduced are new developments in scatterplot smoothing and applications in statistical modelling. The treatment of the topics is on an intermediate level avoiding too much technicalities. Computational and applied aspects are considered throughout. Of particular interest to readers is the discussion of recent local fitting techniques.

• Glättung
• Kernel
• Statistische Modelierung
• Variance
• calculus
• econometrics
• economics
• invariance
• modeling
• regression
• statistical method
• statistical modelling
• statistical theory
• statistics
• Ökonometrie

• Center for Computational Statistics Institut für Statistik und Ökonometrie Wirtschaftswissenschaftliche Fakultät, Humboldt-Universität zu Berlin, Berlin, Germany

  Wolfgang Härdle

• Medical Biometrics Group, University of Graz Medical Schools, Graz, Austria

  Michael G. Schimek

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