000			04141nam a22004815i 4500
001			978-0-387-77501-2
003			DE-He213
005			20251006084414.0
007			cr nn 008mamaa
008			100301s2008 xxu| s |||| 0|eng d
020			_a9780387775012
020			_a99780387775012
024	7		_a10.1007/978-0-387-77501-2 _2doi
100	1		_aBerk, Richard A. _eauthor.
245	1	0	_aStatistical Learning from a Regression Perspective _h[electronic resource] / _cby Richard A. Berk.
264		1	_aNew York, NY : _bSpringer New York, _c2008.
300			_aXVIII, 360p. _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		_aStatistical Learning as a Regression Problem -- Regression Splines and Regression Smoothers -- Classification and Regression Trees (CART) -- Bagging -- Random Forests -- Boosting -- Support Vector Machines -- Broader Implications and a Bit of Craft Lore.
520			_aStatistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical. Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one's data and not apply statistical learning procedures that require more than the data can provide. The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R. Richard Berk is Distinguished Professor of Statistics Emeritus from the Department of Statistics at UCLA and currently a Professor at the University of Pennsylvania in the Department of Statistics and in the Department of Criminology. He is an elected fellow of the American Statistical Association and the American Association for the Advancement of Science and has served in a professional capacity with a number of organizations such as the Committee on Applied and Theoretical Statistics for the National Research Council and the Board of Directors of the Social Science Research Council. His research has ranged across a variety of applications in the social and natural sciences.
650		0	_aSTATISTICS.
650		0	_aMATHEMATICAL STATISTICS.
650		0	_aSOCIAL SCIENCES _xMETHODOLOGY.
650		0	_aPSYCHOLOGICAL TESTS AND TESTING.
650	1	4	_aSTATISTICS.
650	2	4	_aMETHODOLOGY OF THE SOCIAL SCIENCES.
650	2	4	_aPSYCHOLOGICAL METHODS/EVALUATION.
650	2	4	_aPUBLIC HEALTH/GESUNDHEITSWESEN.
650	2	4	_aSTATISTICAL THEORY AND METHODS.
650	2	4	_aSTATISTICS FOR SOCIAL SCIENCE, BEHAVORIAL SCIENCE, EDUCATION, PUBLIC POLICY, AND LAW.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387775005
830		0	_aSpringer Series in Statistics, _x0172-7397
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-77501-2 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c58979 _d58979