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

Peter Bühlmann, Sara van de Geer

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.

「Nielsen BookData」より

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.

「Nielsen BookData」より

[目次]

• 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.

「Nielsen BookData」より

[目次]

• 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.

「Nielsen BookData」より

### 書名 Statistics for high-dimensional data : methods, theory and applications Bühlmann, Peter Geer, S. A. van de Geer Sara Van De Buhlmann Peter Springer series in statistics Springer c2011 xvii, 556 p. 25 cm 9783642268571 9783642201912 BB06071432 ※クリックでCiNii Booksを表示 英語 ドイツ

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