| Description |
xix, 607 pages : illustrations ; 25 cm |
| Series |
Wiley series in probability and statistics. Applied probability and statistics section |
|
Wiley series in probability and statistics. Applied probability and statistics.
|
| Contents |
Machine derived contents note: Spline Regression (R. Eubank). -- Variance Estimation and Smoothing-Parameter Selection for Spline Regression (A. van der Linde). -- Kernel Regression (P. Sarda & P. Vieu). -- Variance Estimation and Bandwidth Selection for Kernel Regression (E. Herrmann). -- Spline and Kernel Regression under Shape Restrictions (M. Delecroix & C. Thomas-Agnan). -- Spline and Kernel Regression for Dependent Data (R. Kohn, et al.). -- Wavelets for Regression and Other Statistical Problems (G. Nason & B. Silverman). -- Smoothing Methods for Discrete Data (J. Simonoff & G. Tutz). -- Local Polynomial Fitting (J. Fan & I. Gijbels). -- Additive and Generalized Additive Models (M. Schimek & B. Turlach). -- Multivariate Spline Regression (C. Gu). -- Multivariate and Semiparametric Kernel Regression (W. Hardle & M. Muller). -- Spatial-Process Estimates as Smoothers (D. Nychka). -- Resampling Methods for Nonparametric Regression (E. Mammen). -- Multidimensional Smoothing and Visualization (D. Scott). -- Projection Pursuit Regression (S. Klinke & J. Grassmann). -- Sliced Inverse Regression (T. Kotter). -- Dynamic and Semiparametric Models (L. Fahrmeir & L. Knorr-Held). -- Nonparametric Bayesian Bivariate Surface Estimation (M. Smith, et al.). -- Index |
| Notes |
"A Wiley-Interscience Publication." |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Nonparametric statistics.
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Regression analysis.
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Smoothing (Statistics)
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| Author |
Schimek, Michael G.
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| LC no. |
99022017 |
| ISBN |
0471179469 (alk. paper) |
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