000			04072cam a22004217i 4500
999			_c185696 _d185695
001			19135591
005			20241226155156.0
008			160614t20162016flua bf 001 0 eng d
010			_a 2016302636
020			_a1482249073
020			_a9781482249071
020			_z9781482249088 (PDF ebook)
035			_a(OCoLC)ocn920852092
040			_beng
042			_alccopycat
050	0	0	_aQA76.9.B45 _bH36 2016
082	0	4	_a005.7 _223 _bHAN
245	0	0	_aHandbook of big data / _cedited by Peter Bühlmann, Petros Drineas, Michael Kane, Mark van der Laan.
264		1	_aBoca Raton, FL : _bCRC Press, an imprint of the Taylor & Francis Group, _c[2016]
264		4	_c©2016
300			_axvi, 464 pages : _billustrations (some color) ; _c26 cm.
490	1		_aChapman & Hall/CRC handbooks of modern statistical methods
500			_a"Chapman & Hall book."
504			_aIncludes bibliographical references and index.
505	0		_aThe advent of data science: some considerations on the unreasonable effectiveness of data / Richard J.C.M. Starmans -- Big-n versus big-p in big data / Norman Matloff -- Divide and recombine: approach for detailed analysis and visualization of large complex data / Ryan Hafen -- Integrate big data for better operation, control, and protection of power systems / Guang Lin -- Interactive visual analysis of big data / Carlos Scheidegger -- A visualization tool for mining large correlation tables: the association navigator / Andreas Buja, Abba M. Krieger, and Edward I. George -- High-dimensional computational geometry / Alexandr Andoni -- IRLBA: fast partial singular value decomposition method / James Baglama -- Structural properties underlying high-quality randomized numerical linear algebra algorithms / Michael W. Mahoney and Petros Drineas -- Something for (almost) nothing: new advances in sublinear-time algorithms / Ronitt Rubinfeld and Eric Blais -- Networks / Elizabeth L. Ogburn and Alexander Volfovsky -- Mining large graphs / David F. Gleich and Michael W. Mahoney -- Estimator and model selection using cross-validation / Iván Díaz -- Stochastic gradient methods for principled estimation with large datasets / Panos Toulis and Edoardo M. Airoldi -- Learning structured distributions / Ilias Diakonikolas -- Penalized estimation in complex methods / Jacob Bien and Daniela Witten -- High-dimensional regression and inference / Lukas Meier -- Divide and recombine: subsemble, exploiting the power of cross-validation / Stephanie Sapp and Erin LeDell -- Scalable super learning / Erin LeDell -- Tutorial for causal inference / Laura Balzer, Maya Petersen, and Mark van der Laan -- A review of some recent advances in causal inference / Marloes H. Maathuis and Preetam Nandy -- Targeted learning for variable importance / Sherri Rose -- Online estimation of the average treatment effect / Sam Lendle -- Mining with inference: data-adaptive target parameters / Alan Hubbard and Mark van der Laan.
520			_a"Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical and computing ideas that can be readily applied in research and practice"-- _cProvided by publisher.
650		0	_aBig data _xStatistical methods _vHandbooks, manuals, etc.
655		7	_aHandbooks and manuals. _2lcgft
700	1		_aBühlmann, Peter,
700	1		_aDrineas, Petros,
700	1		_aKane, Michael _q(Michael John),
700	1		_aLaan, M. J. van der,
830		0	_aChapman & Hall/CRC handbooks of modern statistical methods.
856			_3EB _uhttps://www.taylorfrancis.com/books/edit/10.1201/b19567/handbook-big-data-mark-van-der-laan-petros-drineas-michael-kane-peter-b%C3%BChlmann _ySF
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_cEB