Handbook of big data / edited by Peter Buhlmann, Petros Drineas, Michael Kane, Mark van der Laan.

Format:

Book

Contributor:

Bühlmann, Peter, editor.

Drineas, Petros, editor.

Kane, Michael (Michael John), editor.

Laan, Mark van der, 1978- editor.

Series:

Chapman & Hall/CRC handbooks of modern statistical methods.

Chapman & Hall/CRC Handbooks of Modern Statistical Methods

Language:

English

Subjects (All):

Big data--United States.

Big data.

Big data--Statistical methods.

Physical Description:

1 online resource (470 p.)

Edition:

1st ed.

Place of Publication:

Boca Raton, FL : CRC Press, [2016]

Language Note:

English

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"-- Provided by publisher.

Contents:

Front Cover; Contents; Preface; Editors; Contributors; I. General Perspectives on Big Data; 1. The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data; 2. Big-n versus Big-p in Big Data; II. Data-Centric, Exploratory Methods; 3. Divide and Recombine: Approach for Detailed Analysis and Visualization of Large Complex Data; 4. Integrate Big Data for Better Operation, Control, and Protection of Power Systems; 5. Interactive Visual Analysis of Big Data; 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator

III. Efficient Algorithms7. High-Dimensional Computational Geometry; 8. IRLBA: Fast Partial Singular Value Decomposition Method; 9. Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms; 10. Something for (Almost) Nothing: New Advances in Sublinear-Time Algorithms; IV. Graph Approaches; 11. Networks; 12. Mining Large Graphs; V. Model Fitting and Regularization; 13. Estimator and Model Selection Using Cross-Validation; 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets; 15. Learning Structured Distributions

16. Penalized Estimation in Complex Models17. High-Dimensional Regression and Inference; VI. Ensemble Methods; 18. Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation; 19. Scalable Super Learning; VII. Causal Inference; 20. Tutorial for Causal Inference; 21. A Review of Some Recent Advances in Causal Inference; VIII. Targeted Learning; 22. Targeted Learning for Variable Importance; 23. Online Estimation of the Average Treatment Effect; 24. Mining with Inference: Data-Adaptive Target Parameters; Back Cover

Notes:

A Chapman and Hall book.

Description based on online resource; title from PDF title page (ebrary, viewed June 2, 2016).

ISBN:

9781040072387

1040072380

9780429162985

0429162987

9781482249088

1482249081

OCLC:

939597365