TY  - BOOK
AU  - Buhlmann,Peter
AU  - Drineas,Petros
AU  - Kane,Michael
AU  - Laan,Mark van der
TI  - Handbook of big data
T2  - Chapman & Hall/CRC handbooks of modern statistical methods
SN  - 9781482249071
U1  - 005.7 23
PY  - 2016///
CY  - Boca Raton : 
PB  - CRC Press,  
KW  - Big data
KW  - Statistical methods
KW  - Handbooks, manuals, etc
KW  - Handbooks and manuals
KW  - lcgft
N1  - Includes bibliographical references and index; 1. The advent of data science: some considerations on the unreasonable effectiveness of data / Richard J.C.M. Starmans --
2. Big-n versus big-p in big data / Norman Matloff --
3. Divide and recombine: approach for detailed analysis and visualization of large complex data / Ryan Hafen --
4. Integrate big data for better operation, control, and protection of power systems / Guang Lin --
5. Interactive visual analysis of big data / Carlos Scheidegger --
6. A visualization tool for mining large correlation tables: the association navigator / Andreas Buja, Abba M. Krieger, and Edward I. George --
7. High-dimensional computational geometry / Alexandr Andoni --
8. IRLBA: fast partial singular value decomposition method / James Baglama --
9. Structural properties underlying high-quality randomized numerical linear algebra algorithms / Michael W. Mahoney and Petros Drineas --
10. Something for (almost) nothing: new advances in sublinear-time algorithms / Ronitt Rubinfeld and Eric Blais --
11. Networks / Elizabeth L. Ogburn and Alexander Volfovsky --
12. Mining large graphs / David F. Gleich and Michael W. Mahoney --
13. Estimator and model selection using cross-validation / Ivan Diaz --
14. Stochastic gradient methods for principled estimation with large datasets / Panos Toulis and Edoardo M. Airoldi --
15. Learning structured distributions / Ilias Diakonikolas --
16. Penalized estimation in complex methods / Jacob Bien and Daniela Witten --
17. High-dimensional regression and inference / Lukas Meier --
18. Divide and recombine: subsemble, exploiting the power of cross-validation / Stephanie Sapp and Erin LeDell --
19. Scalable super learning / Erin LeDell --
20. Tutorial for causal inference / Laura Balzer, Maya Petersen, and Mark van der Laan --
21. A review of some recent advances in causal inference / Marloes H. Maathuis and Preetam Nandy --
22. Targeted learning for variable importance / Sherri Rose --
23. Online estimation of the average treatment effect / Sam Lendle --
24. Mining with inference: data-adaptive target parameters / Alan Hubbard and Mark van der Laan
N2  - 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
ER  -