Statistics for High-Dimensional Data. Este artículo no está disponible.
Peter Bühlmann, Sara van de Geer
Idioma: inglés
Editorial: Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2011
- Tapa dura
- Nuevo
Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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Descripción del artículo del vendedor
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, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections.A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods' great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.
N° de ref. del artículo LU-9783642201912
- Título
- Statistics for High-Dimensional Data
- Autor
- Peter Bühlmann, Sara van de Geer
- Editorial
- Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE
- Año de publicación
- 2011
- Estado
- New
- Encuadernación
- Hardback
- Idioma
- inglés
- ISBN 10
- 3642201911
- ISBN 13
- 9783642201912
- Serie
- Libro 123 de 160: Springer Series in Statistics
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, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections.
A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.
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