Handbook of big data

✍ Scribed by Bühlmann, Peter

• Publisher: CRC Press
• Year: 2016
• Tongue: English
• Leaves: 470
• Series: Chapman & Hall/CRC handbooks of modern statistical methods
• Category: Library

✦ Synopsis

"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"--;I. General perspectives on big data -- II. Data-centric, exploratory methods -- III. Efficient algorithms -- IV. Graph approaches -- V. Model fitting and regularization -- VI. Ensemble methods -- VII. Causal inference -- VIII. Targeted learning.

✦ Table of Contents

Front Cover......Page 1
Contents......Page 8
Preface......Page 12
Editors......Page 14
Contributors......Page 16
I. General Perspectives on Big Data......Page 18
1. The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data......Page 20
2. Big-n versus Big-p in Big Data......Page 38
II. Data-Centric, Exploratory Methods......Page 50
3. Divide and Recombine: Approach for Detailed Analysis and Visualization of Large Complex Data......Page 52
4. Integrate Big Data for Better Operation, Control, and Protection of Power Systems......Page 64
5. Interactive Visual Analysis of Big Data......Page 78
6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator......Page 90
III. Efficient Algorithms......Page 120
7. High-Dimensional Computational Geometry......Page 122
8. IRLBA: Fast Partial Singular Value Decomposition Method......Page 142
9. Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms......Page 154
10. Something for (Almost) Nothing: New Advances in Sublinear-Time Algorithms......Page 172
IV. Graph Approaches......Page 186
11. Networks......Page 188
12. Mining Large Graphs......Page 208
V. Model Fitting and Regularization......Page 238
13. Estimator and Model Selection Using Cross-Validation......Page 240
14. Stochastic Gradient Methods for Principled Estimation with Large Datasets......Page 258
15. Learning Structured Distributions......Page 284
16. Penalized Estimation in Complex Models......Page 302
17. High-Dimensional Regression and Inference......Page 322
VI. Ensemble Methods......Page 338
18. Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation......Page 340
19. Scalable Super Learning......Page 356
VII. Causal Inference......Page 376
20. Tutorial for Causal Inference......Page 378
21. A Review of Some Recent Advances in Causal Inference......Page 404
VIII. Targeted Learning......Page 426
22. Targeted Learning for Variable Importance......Page 428
23. Online Estimation of the Average Treatment Effect......Page 446
24. Mining with Inference: Data-Adaptive Target Parameters......Page 456
Back Cover......Page 470