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ORCIDHandbook of Big Data 2016
- Peter Bühlmann, Petros Drineas, Michael J. Kane, Mark J. van der Laan:
Handbook of Big Data. Chapman and Hall/CRC 2016, ISBN 978-1-4822-4907-1
General Perspectives on Big Data
- Richard J. C. M. Starmans:
The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data. 3-19 - Norman S. Matloff:
Big-n versus Big-p in Big Data. 21-32
Data-Centric, Exploratory Methods
- Ryan Hafen:
Divide and Recombine: Approach for Detailed Analysis and Visualization of Large Complex Data. 35-46 - Guang Lin:
Integrate Big Data for Better Operation, Control, and Protection of Power Systems. 47-60 - Carlos Scheidegger:
Interactive Visual Analysis of Big Data. 61-71 - Andreas Buja, Abba M. Krieger, Edward I. George:
A Visualization Tool for Mining Large Correlation Tables: The Association Navigator. 73-102
Efficient Algorithms
- Alexandr Andoni:
High-Dimensional Computational Geometry. 105-123 - James Baglama:
IRLBA: Fast Partial Singular Value Decomposition Method. 125-136 - Michael W. Mahoney, Petros Drineas
:
Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms. 137-154 - Ronitt Rubinfeld, Eric Blais:
Something for (Almost) Nothing: New Advances in Sublinear-Time Algorithms. 155-167
Graph Approaches
- Elizabeth L. Ogburn, Alexander Volfovsky:
Networks. 171-190 - David F. Gleich, Michael W. Mahoney:
Mining Large Graphs. 191-220
Model Fitting and Regularization
- Iván Díaz:
Estimator and Model Selection Using Cross-Validation. 223-239 - Panos Toulis, Edoardo M. Airoldi:
Stochastic Gradient Methods for Principled Estimation with Large Datasets. 241-265 - Ilias Diakonikolas:
Learning Structured Distributions. 267-283 - Jacob Bien, Daniela M. Witten:
Penalized Estimation in Complex Models. 285-303 - Lukas Meier:
High-Dimensional Regression and Inference. 305-319
Ensemble Methods
- Stephanie Sapp, Erin LeDell:
Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation. 323-338 - Erin LeDell:
Scalable Super Learning. 339-357
Causal Inference
- Laura Balzer, Maya Petersen, Mark J. van der Laan:
Tutorial for Causal Inference. 361-386 - Marloes H. Maathuis, Preetam Nandy:
A Review of Some Recent Advances in Causal Inference. 387-407
Targeted Learning
- Sherri Rose:
Targeted Learning for Variable Importance. 411-427 - Sam Lendle:
Online Estimation of the Average Treatment Effect. 429-438 - Alan E. Hubbard, Mark J. van der Laan:
Mining with Inference: Data-Adaptive Target Parameters. 439-452 - Index. 453-464
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