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Item Details
Title:
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STATISTICS FOR HIGH-DIMENSIONAL DATA
METHODS, THEORY AND APPLICATIONS |
By: |
Peter Buhlmann, Sara van de Geer |
Format: |
Hardback |
List price:
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£109.99 |
We currently do not stock this item, please contact the publisher directly for
further information.
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ISBN 10: |
3642201911 |
ISBN 13: |
9783642201912 |
Publisher: |
SPRINGER-VERLAG BERLIN AND HEIDELBERG GMBH & CO. KG |
Pub. date: |
8 June, 2011 |
Edition: |
2011 ed. |
Series: |
Springer Series in Statistics |
Pages: |
558 |
Description: |
This valuable compendium of statistical methods features a unique combination of methodology, theory, algorithms and applications. It covers recently developed approaches to handling large and complex data sets, including the Lasso and boosting methods. |
Synopsis: |
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. |
Illustrations: |
XVIII, 558 p. |
Publication: |
Germany |
Imprint: |
Springer-Verlag Berlin and Heidelberg GmbH & Co. K |
Returns: |
Returnable |
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