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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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Previews available in: English
Showing 2 featured editions. View all 2 editions?
Edition | Availability |
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1
Statistics for High-Dimensional Data: Methods, Theory and Applications
Aug 03, 2013, Springer
paperback
3642268579 9783642268571
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2
Statistics for High-Dimensional Data: Methods, Theory and Applications
2011, Springer-Verlag Berlin Heidelberg
electronic resource :
in English
3642201911 9783642201912
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- Created July 29, 2014
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August 22, 2024 | Edited by MARC Bot | import existing book |
February 26, 2022 | Edited by ImportBot | import existing book |
September 11, 2021 | Edited by ImportBot | import existing book |
October 17, 2020 | Edited by MARC Bot | import existing book |
July 29, 2014 | Created by ImportBot | Imported from Internet Archive item record |