000			04820nam a22005415i 4500
001			978-0-387-98144-4
003			DE-He213
005			20251006084433.0
007			cr nn 008mamaa
008			100715s2009 xxu| s |||| 0|eng d
020			_a9780387981444
020			_a99780387981444
024	7		_a10.1007/978-0-387-98144-4 _2doi
082	0	4	_a519.5 _223
100	1		_aGentle, James E. _eauthor.
245	1	0	_aComputational Statistics _h[electronic resource] / _cby James E. Gentle.
264		1	_aNew York, NY : _bSpringer New York, _c2009.
300			_aXXII, 728 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStatistics and Computing, _x1431-8784
505	0		_aPreliminaries -- Mathematical and Statistical Preliminaries -- Statistical Computing -- Computer Storage and Arithmetic -- Algorithms and Programming -- Approximation of Functions and Numerical Quadrature -- Numerical Linear Algebra -- Solution of Nonlinear Equations and Optimization -- Generation of Random Numbers -- Methods of Computational Statistics -- Graphical Methods in Computational Statistics -- Tools for Identification of Structure in Data -- Estimation of Functions -- Monte Carlo Methods for Statistical Inference -- Data Randomization, Partitioning, and Augmentation -- Bootstrap Methods -- Exploring Data Density and Relationships -- Estimation of Probability Density Functions Using Parametric Models -- Nonparametric Estimation of Probability Density Functions -- Statistical Learning and Data Mining -- Statistical Models of Dependencies.
520			_aComputational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally-intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods. The book assumes an intermediate background in mathematics, computing, and applied and theoretical statistics. The first part of the book, consisting of a single long chapter, reviews this background material while introducing computationally-intensive exploratory data analysis and computational inference. The six chapters in the second part of the book are on statistical computing. This part describes arithmetic in digital computers and how the nature of digital computations affects algorithms used in statistical methods. Building on the first chapters on numerical computations and algorithm design, the following chapters cover the main areas of statistical numerical analysis, that is, approximation of functions, numerical quadrature, numerical linear algebra, solution of nonlinear equations, optimization, and random number generation. The third and fourth parts of the book cover methods of computational statistics, including Monte Carlo methods, randomization and cross validation, the bootstrap, probability density estimation, and statistical learning. The book includes a large number of exercises with some solutions provided in an appendix. James E. Gentle is University Professor of Computational Statistics at George Mason University. He is a Fellow of the American Statistical Association (ASA) and of the American Association for the Advancement of Science. He has held several national offices in the ASA and has served as associate editor of journals of the ASA as well as for other journals in statistics and computing. He is author of Random Number Generation and Monte Carlo Methods and Matrix Algebra.
650		0	_aSTATISTICS.
650		0	_aELECTRONIC DATA PROCESSING.
650		0	_aDATA MINING.
650		0	_aCOMPUTER SIMULATION.
650		0	_aNUMERICAL ANALYSIS.
650		0	_aMATHEMATICAL STATISTICS.
650		0	_aENGINEERING MATHEMATICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICS AND COMPUTING/STATISTICS PROGRAMS.
650	2	4	_aNUMERIC COMPUTING.
650	2	4	_aDATA MINING AND KNOWLEDGE DISCOVERY.
650	2	4	_aSIMULATION AND MODELING.
650	2	4	_aAPPL.MATHEMATICS/COMPUTATIONAL METHODS OF ENGINEERING.
650	2	4	_aNUMERICAL ANALYSIS.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387981437
830		0	_aStatistics and Computing, _x1431-8784
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-98144-4 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c59588 _d59588