Statistics for High-Dimensional Data: Methods, Theory and Applications. Peter BГјhlmann, Sara van de Geer

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


Statistics.for.High.Dimensional.Data.Methods.Theory.and.Applications.pdf
ISBN: 3642201911,9783642201929 | 575 pages | 29 Mb

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



Statistics for High-Dimensional Data: Methods, Theory and Applications Peter BГјhlmann, Sara van de Geer
Publisher: Springer


From the reviews: “This book is a complete study of ℓ1-penalization based statistical methods for high-dimensional data … . Definitely, this book is useful. … its strong level in mathematics makes it more suitable to researchers and graduate students who already have a strong background in statistics. … it gives the state-of-the-art of the theory, and therefore can be used for an advanced course on the topic. … the last part of the book is an exciting introduction to new research perspectives provided by ℓ1-penalized methods.” (Pierre Alquier, Mathematical Reviews, Issue 2012 e) 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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