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Statistics for high-dimensional data : methods, theory and applications /

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, such as the Lasso and boosting methods. It also provides the mathematical theory behind them, provin...

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Bibliographic Details
Main Author: Bühlmann, Peter
Other Authors: Geer, S. A. van de (Sara A.)
Format: eBook
Language:English
Published: Heidelberg ; New York : Springer, [2011]
Series:Springer series in statistics.
Subjects:
Least absolute deviations (Statistics)
Linear models (Statistics)
Mathematical statistics.
Nonconvex programming.
Smoothness of functions.
Database management > Quality control.
Electronic data processing > Quality control.
Mathematics.
Electronic books.
Online Access:Connect to the full text of this electronic book
Description
Summary: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, such as the Lasso and boosting methods. It also provides the mathematical theory behind them, proving their great potential in a large number of settings. Both the methods and theory are then illustrated with real data examples.
Item Description:Electronic resource.
Physical Description:1 online resource.
Bibliography:Includes bibliographical references (pages 547-556) and indexes.
ISBN:364220192X (electronic bk.)
9783642201929 (electronic bk.)
DOI:10.1007/978-3-642-20192-9