Statistics for High-Dimensional Data
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<p>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.<br>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.</p>
- Autor: Sara van de Geer / Peter Bühlmann
- Seitenzahl: 558
- Format: PDF
- DRM: social-drm (ohne Kopierschutz)
- Erscheinungsdatum: 08.06.2011
- Herausgeber: SPRINGER