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Statistics for High-Dimensional Data : Methods, Theory and Applications - Peter Bühlmann

Statistics for High-Dimensional Data

Methods, Theory and Applications

By: Peter Bühlmann, Sara van de Geer

eText | 8 June 2011

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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 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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Non-FictionComputing & I.T.Computer ScienceBusiness ApplicationsMathematical & Statistical SoftwareMathematicsProbability & Statistics

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Published: 3rd August 2013

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