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...
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Auteurs principaux : | , |
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Format : | Livre |
Langue : | anglais |
Titre complet : | Maximum penalized likelihood estimation : Volume II: regression / by Vincent N. LaRiccia, Paul P. Eggermont |
Publié : |
New York, NY :
Springer New York
, 2009 Springer e-books |
Collection : | Springer series in statistics Mathematics and Statistics |
Disponibilité : | L'accès complet au document est réservé aux usagers des établissements qui en ont fait l'acquisition |
Contenu : | 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 |
Sujets : | |
Documents associés : | Regression:
Maximum penalized likelihood estimation |
Bib. CRDM (Mathématiques)
| Cote | Prêt | Statut |
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Bibliothèque | 62C453 | Prêt sans prolongation | Disponible |