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

August 3, 2013|
Statistics for High-Dimensional Data: Methods, Theory and Applications by 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 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.

Peter Bühlmann is Professor of Statistics at ETH Zürich. His main research areas are high-dimensional statistical inference, machine learning, graphical modeling, nonparametric methods, and statistical modeling in the life sciences. He is currently editor of the Annals of Statistics. He was awarded a Medallion lecture by the ...
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Title:Statistics for High-Dimensional Data: Methods, Theory and Applications
Format:Paperback
Product dimensions:558 pages, 9.25 X 6.1 X 0 in
Shipping dimensions:558 pages, 9.25 X 6.1 X 0 in
Published:August 3, 2013
Publisher:Springer-Verlag/Sci-Tech/Trade
Language:English
Appropriate for ages:All ages
ISBN - 13:9783642268571

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