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" Handbook of big data / "
edited by Peter Bühlmann, Petros Drineas, Michael Kane, Mark van der Laan
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BL
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| Record Number
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667859
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| Doc. No
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dltt
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| Title & Author
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Handbook of big data /\ edited by Peter Bühlmann, Petros Drineas, Michael Kane, Mark van der Laan
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| Series Statement
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Chapman & Hall/CRC handbooks of modern statistical methods
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| Page. NO
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xvi, 464 pages :: illustrations (some color) ;; 26 cm
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| ISBN
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1482249073
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: 9781482249071
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| Notes
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"Chapman & Hall book."
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| Bibliographies/Indexes
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Includes bibliographical references and index
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| Contents
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The 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
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| Abstract
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"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"--Back cover
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| Subject
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Big data-- Statistical methods, Handbooks, manuals, etc
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| LC Classification
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QA76.9.B45H36 2016
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| Added Entry
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Bühlmann, Peter
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Drineas, Petros
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Kane, Michael
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Laan, M. J. van der
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