02811nam a22004215i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153072001600167072002300183082001400206100003200220245014200252264006100394300003600455336002600491337002600517338003600543347002400579490004600603505054800649520082701197650001602024650002902040650001602069650003602085650005202121700003202173710003402205773002002239776003602259830004602295856004802341978-3-642-20192-9DE-He21320180115171726.0cr nn 008mamaa110719s2011 gw | s |||| 0|eng d a97836422019297 a10.1007/978-3-642-20192-92doi 4aQA276-280 7aPBT2bicssc 7aMAT0290002bisacsh04a519.52231 aBühlmann, Peter.eauthor.10aStatistics for High-Dimensional Datah[electronic resource] :bMethods, Theory and Applications /cby Peter Bühlmann, Sara van de Geer. 1aBerlin, Heidelberg :bSpringer Berlin Heidelberg,c2011. aXVIII, 558 p.bonline resource. atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier atext filebPDF2rda1 aSpringer Series in Statistics,x0172-73970 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. 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 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. 0aStatistics. 0aMathematical statistics.14aStatistics.24aStatistical Theory and Methods.24aProbability and Statistics in Computer Science.1 avan de Geer, Sara.eauthor.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9783642201912 0aSpringer Series in Statistics,x0172-739740uhttp://dx.doi.org/10.1007/978-3-642-20192-9