000 04379cam a22002537i 4500
008 160614t20162016flua bf 001 0 eng d
020 _a9781482249071
082 0 4 _a005.7
_bH2
245 0 0 _aHandbook of big data
260 _aBoca Raton
_bCRC Press
_c2016
300 _axvi, 464 p.
440 _aChapman & Hall/CRC handbooks of modern statistical methods
_9321791
500 _a"Chapman & Hall book."
504 _aTable of Contents 1. GENERAL PERSPECTIVES ON BIG DATA The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data Richard Starmans Big n versus Big p in Big Data Norman Matloff 2. DATA-CENTRIC, EXPLORATORY METHODS 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 3. EFFICIENT ALGORITHMS High-Dimensional Computational Geometry Alexandr Andoni IRLBA: Fast Partial SVD 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 4. GRAPH APPROACHES Networks Elizabeth L. Ogburn and Alexander Volfovsky Mining Large Graphs David F. Gleich and Michael W. Mahoney 5. MODEL FITTING AND REGULARIZATION 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 Models Jacob Bien and Daniela Witten High-Dimensional Regression and Inference Lukas Meier 6. ENSEMBLE METHODS Divide and Recombine Subsemble, Exploiting the Power of Cross-Validation Stephanie Sapp and Erin LeDell Scalable Super Learning Erin LeDell 7. CAUSAL INFERENCE 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 8. TARGETED LEARNING 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 _aFeatures Depicts the current landscape of big data analysis Emphasizes computational statistics and machine learning Strikes the right balance not only between statistical theory and applications in computer science but also between the breadth of topics and the depth to which each topic is explored Summary 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. Offering balanced coverage of methodology, theory, and applications, this handbook: Describes modern, scalable approaches for analyzing increasingly large datasets Defines the underlying concepts of the available analytical tools and techniques Details intercommunity advances in computational statistics and machine learning Handbook of Big Data also identifies areas in need of further development, encouraging greater communication and collaboration between researchers in big data sub-specialties such as genomics, computational biology, and finance. https://www.crcpress.com/Handbook-of-Big-Data/Buhlmann-Drineas-Kane-van-der-Laan/p/book/9781482249071
650 0 _aBig data
_9338776
650 0 _aStatistical methods
_918522
650 0 _aHandbooks - Manuals
_929873
700 1 _aBuhlmann, Peter
_eEditor
_9338777
700 1 _aDrineas, Petros
_eEditor
_9338778
700 1 _aKane, Michael
_eEditor
_9338779
700 1 _aLaan, M. J. van der
_eEditor
_9338780
942 _cBK
999 _c205219
_d205219