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020			_a9780387981444 _99780387981444
024	7		_a10.1007/9780387981444 _2doi
035			_avtls000333440
039		9	_a201509030231 _bVLOAD _c201404130438 _dVLOAD _c201404092228 _dVLOAD _y201402041111 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aGentle, James E. _eautor _9305300
245	1	0	_aComputational Statistics / _cby James E. Gentle.
264		1	_aNew York, NY : _bSpringer New York, _c2009.
300			_axxii, 728 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aStatistics and Computing, _x1431-8784
500			_aSpringer eBooks
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.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387981437
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-98144-4 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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