000			03394nam a22004455i 4500
001			978-0-387-30623-0
003			DE-He213
005			20250710083947.0
007			cr nn 008mamaa
008			100301s2006 xxu| s |||| 0|eng d
020			_a9780387306230 _a99780387306230
024	7		_a10.1007/0-387-30623-4 _2doi
082	0	4	_a519.5 _223
100	1		_aWasserman, Larry. _eauthor.
245	1	0	_aAll of Nonparametric Statistics _h[recurso electrónico] / _cby Larry Wasserman.
264		1	_aNew York, NY : _bSpringer New York, _c2006.
300			_aXII, 268 p. 52 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Texts in Statistics, _x1431-875X
505	0		_aEstimating the CDF and Statistical Functionals -- The Bootstrap and the Jackknife -- Smoothing: General Concepts -- Nonparametric Regression -- Density Estimation -- Normal Means and Minimax Theory -- Nonparametric Inference Using Orthogonal Functions -- Wavelets and Other Adaptive Methods -- Other Topics.
520			_aThe goal of this text is to provide the reader with a single book where they can find a brief account of many, modern topics in nonparametric inference. The book is aimed at Master's level or Ph.D. level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. This text covers a wide range of topics including: the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book has a mixture of methods and theory. Larry Wasserman is Professor of Statistics at Carnegie Mellon University and a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, multiple testing, and applications to astrophysics, bioinformatics and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathématiques de Montreal-Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics. He is the author of All of Statistics: A Concise Course in Statistical Inference (Springer, 2003).
650		0	_aSTATISTICS.
650		0	_aARTIFICIAL INTELLIGENCE.
650		0	_aMATHEMATICAL STATISTICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICAL THEORY AND METHODS.
650	2	4	_aARTIFICIAL INTELLIGENCE (INCL. ROBOTICS).
650	2	4	_aSTATISTICS FOR ENGINEERING, PHYSICS, COMPUTER SCIENCE, CHEMISTRY & GEOSCIENCES.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387251455
830		0	_aSpringer Texts in Statistics, _x1431-875X
856	4	0	_uhttp://dx.doi.org/10.1007/0-387-30623-4 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c57065 _d57065