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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...
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| Auteurs principaux : | , |
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| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Statistics for high-dimensional data : methods, theory and applications / Peter Bühlmann, Sara van de Geer |
| Publié : |
Berlin, London, New York [etc.] :
Springer
, cop. 2011, cop. 2011 |
| Description matérielle : | 1 vol. (XVII-556 p.) |
| Collection : | Springer series in statistics |
| Contenu : | Contient des exercices |
| Sujets : | |
| Documents associés : | Autre format:
Statistics for high-dimensional data |
| 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 |
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| Notes : | Résumés en anglais |
| Bibliographie : | Bibliogr. p. 547-556. Index |
| ISBN : | 978-3-642-20191-2 3-642-20191-1 978-3-642-26857-1 |