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	Title:Statistics for High-Dimensional Data: Methods, Theory and Applications (Springer Series in Statistics) Author:Peter Buhlmann, Sara van de Geer Publisher:Springer ISBN:3642201911 ISBN13: Date2011-06-14 Pages:573 Language:English Format: PDF Size:5,5 MB
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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