Statistics for High-Dimensional Data: Methods, Theory and Applications - Sara van de Geer,Peter Bühlmann
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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 ... Full description
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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.
More Information
| Author | Sara van de Geer, Peter Bühlmann |
|---|---|
| Publisher | Springer Berlin Heidelberg |
| Series | Springer Series in Statistics |
| Release year | 2013 |
| Cover type | Softcover |
| EAN | 9783642268571 |