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MARC 21

Statistics for High-Dimensional Data: Methods, Theory and Applications
Tag	Description
020	$a9783642201929
082	$a519.5 (DDC 23)
099	$aOnline Resource : Springer
100	$aBühlmann, Peter
245	$aStatistics for High-Dimensional Data$hEB$bMethods, Theory and Applications$cby Peter Bühlmann, Sara van de Geer.
260	$aBerlin, Heidelberg$bSpringer$c2011.
300	$aXVII, 556p. 31 illus., 8 illus. in color.$bonline resource.
336	$atext
338	$aonline resource
440	$aSpringer Series in Statistics,$x0172-7397
505	$aIntroduction -- 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.
520	$aModern 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 methodsâ 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.
538	$aOnline access is restricted to subscription insitutions through IP address (only for SISSA internal users)
700	$avan de Geer, Sara.$eauthor.
710	$aSpringerLink (Online service)
830	$aSpringer Series in Statistics,
856	$uhttp://dx.doi.org/10.1007/978-3-642-20192-9