Maximum penalized likelihood estimation : Volume II: regression
This is the second volume of a text on the theory and practice of maximum penalized likelihood estimation. It is intended for graduate students in statistics, operations research and applied mathematics, as well as for researchers and practitioners in the field. The present volume deals with nonpara...
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Édition: | 1st ed. 2009. |
| Collection: | Springer Series in Statistics
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| Sujets: | |
| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Note: |
Description d'après consultation du 06 juillet 2011 Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Maximum penalized likelihood estimation, volume II, Regression, P.P.B. Eggermont, V.N. LaRiccia, New York, Springer, 2009, 1 vol. (XII-571 p.), Springer series in statistics, 978-0-387-95268-0 |
Table des matières:
- Nonparametric Regression Smoothing Splines Kernel Estimators Sieves Local Polynomial Estimators Other Nonparametric Regression Problems Smoothing Parameter Selection Computing Nonparametric Estimators Kalman Filtering for Spline Smoothing Equivalent Kernels for Smoothing Splines Strong Approximation and Confidence Bands Nonparametric Regression in Action