000			02979nam a22004695i 4500
001			978-3-642-20192-9
003			DE-He213
005			20140220083800.0
007			cr nn 008mamaa
008			110719s2011 gw | s |||| 0|eng d
020			_a9783642201929 _9978-3-642-20192-9
024	7		_a10.1007/978-3-642-20192-9 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aBühlmann, Peter. _eauthor.
245	1	0	_aStatistics for High-Dimensional Data _h[electronic resource] : _bMethods, Theory and Applications / _cby Peter Bühlmann, Sara van de Geer.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aXVIII, 558 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Series in Statistics, _x0172-7397
505	0		_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 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.
650		0	_aStatistics.
650		0	_aComputer science.
650		0	_aMathematical statistics.
650	1	4	_aStatistics.
650	2	4	_aStatistical Theory and Methods.
650	2	4	_aProbability and Statistics in Computer Science.
700	1		_avan de Geer, Sara. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642201912
830		0	_aSpringer Series in Statistics, _x0172-7397
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-20192-9
912			_aZDB-2-SMA
999			_c107733 _d107733