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＜電子ブック＞
Statistics for High-Dimensional Data : Methods, Theory and Applications / by Peter Bühlmann, Sara van de Geer
(Springer Series in Statistics. ISSN:2197568X)

版	1st ed. 2011.
出版者	(Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer)
出版年	2011
本文言語	英語
大きさ	XVIII, 558 p : online resource
著者標目	*Bühlmann, Peter author van de Geer, Sara author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Computer science -- Mathematics  全ての件名で検索 LCSH:Mathematical statistics FREE:Statistical Theory and Methods FREE:Probability and Statistics in Computer Science
一般注記	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 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 HTTP:URL=https://doi.org/10.1007/978-3-642-20192-9

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