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, proving their great potential in a large number of settings. Both the methods and theory are then illustrated with real data examples.
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- Full title :Statistics for high-dimensional data : methods, theory and applications / Peter Bühlmann, Sara van de Geer.
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.
Read More ↓Edition Statements
- Publication Date:©2011.
Publication Statements
- Publisher(s):Springer
- Place of Publication:Heidelberg
- Language:eng
- Publication Date:©2011.
The Physical Object
- Description:1 online resource (xvii, 556 pages) : : illustrations
Edition Identifiers
- ISBN:9783642201929, 364220192X
- DOI:10.1007/978-3-642-20192-9.
- oclc:ocn752337613
- LC Classification:QA276
- Dewey Decimal:519.5
Table of Contents
Introduction -- Lasso for linear models -- Generalized linear models and the Lasso -- The group Lasso -- Additive models and many smooth univariate functions -- Theory for the Lasso -- Variable selection with the Lasso -- Theory for l1/l2-penalty procedures -- Non-convex loss functions and l1-regularization -- Stable solutions -- P-values for linear models and beyond -- Boosting and greedy algorithms -- Graphical modeling -- Probability and moment inequalities -- Author Index -- Index -- References -- Problems at the end of each chapter.
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