Statistics for high-dimensional data : methods, theory and applications
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, provin...
Kaydedildi:
| Asıl Yazarlar: | , , |
|---|---|
| Materyal Türü: | Livre numérique |
| Dil: | Anglais |
| Baskı/Yayın Bilgisi: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2011.
Cham : Springer Nature |
| Seri Bilgileri: | Springer Series in Statistics
|
| Konular: | |
| Online Erişim: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Not: |
Description d'après consultation du 22 avril 2013 Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Statistics for high-dimensional data, methods, theory and applications, Peter Bühlmann, Sara van de Geer, 2011, Berlin, Springer, 1 vol. (XVII-556 p.), Springer series in statistics, 978-3-642-20191-2 |
İçindekiler:
- Introduction Lasso for linear models Generalized linear models and the Lasso The group Lasso Additive models and many smooth univariate functions Theory for the Lasso Variable selection with the Lasso Theory for l1/l2-penalty procedures Non-convex loss functions and l1-regularization Stable solutions P-values for linear models and beyond Boosting and greedy algorithms Graphical modeling Probability and moment inequalities Author Index Index References Problems at the end of each chapter