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Handbook of big data /
"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...
| Other Authors: | , , , |
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| Format: | Printed Book |
| Series: | Chapman & Hall/CRC handbooks of modern statistical methods
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| Subjects: |
Table of Contents:
- 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.