Statistics for High-Dimensional Data: Methods, Theory and Applications
- ISBN-10
- 3642201911
- ISBN-13
- 9783642201912
- Category
- Mathematics
- Pages
- 558
- Language
- English
- Published
- 2011-06-08
- Publisher
- Springer
- Authors
- Peter Bühlmann, Sara van de Geer
Description
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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Other editions
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- 2011-06-08
- 558 pages
- Ebook
- Springer Science & Business Media
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