Statistics for high-dimensional data : methods, theory and applications

Auteur :
Bühlmann Peter
Geer Sara A. van de, auteur
Publication :
Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg : Springer e-books, 2011
Collection :
Springer Series in Statistics
Note :
Numerisation de l'édition de : New York : Springer Science+Business Media, LLC et Springer e-books, 2011
Bibliogr. Index
Résumé :
Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, such as the Lasso and boosting methods. It also provides the mathematical theory behind them, proving their great potential in a large number of settings. Both the methods and theory are then illustrated with real data examples.
Disponible en ligne
364-220-191-1 et 978-3-642-20192-9
Langue :
Type de document :
Ressource électronique
Thèmes :
Fonctions de lissage
Programmation non convexe
Moindres valeurs absolues
Modèles linéaires (statistique)
Computer science
Mathematical statistics
Statistical Theory and Methods
Probability and Statistics in Computer Science