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Handbook of Big Data

Edited by: Bühlmann Peter , Drineas Petros , Kane Michael , van der Laan Mark

Print publication date:  February  2016
Online publication date:  February  2016

Print ISBN: 9781482249071
eBook ISBN: 9781482249088
Adobe ISBN:

 Cite  Marc Record

Book description

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.

Offering balanced coverage of methodology, theory, and applications, this handbook:

  • Describes modern, scalable approaches for analyzing increasingly large datasets
  • Defines the underlying concepts of the available analytical tools and techniques
  • Details intercommunity advances in computational statistics and machine learning

Handbook of Big Data also identifies areas in need of further development, encouraging greater communication and collaboration between researchers in big data sub-specialties such as genomics, computational biology, and finance.

Table of contents

Chapter  1:  The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data Download PDF
Chapter  2:  Big-n versus Big-p in Big Data  Download PDF
Chapter  3:  Divide and Recombine: Approach for Detailed Analysis and Visualization of Large Complex Data Download PDF
Chapter  4:  Integrate Big Data for Better Operation, Control, and Protection of Power Systems Download PDF
Chapter  5:  Interactive Visual Analysis of Big Data Download PDF
Chapter  6:  A Visualization Tool for Mining Large Correlation Tables: The Association Navigator Download PDF
Chapter  7:  High-Dimensional Computational Geometry Download PDF
Chapter  8:  IRLBA: Fast Partial Singular Value Decomposition Method Download PDF
Chapter  9:  Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms Download PDF
Chapter  10:  Something for (Almost) Nothing: New Advances in Sublinear-Time Algorithms Download PDF
Chapter  11:  Networks Download PDF
Chapter  12:  Mining Large Graphs Download PDF
Chapter  13:  Estimator and Model Selection Using Cross-Validation Download PDF
Chapter  14:  Stochastic Gradient Methods for Principled Estimation with Large Datasets Download PDF
Chapter  15:  Learning Structured Distributions Download PDF
Chapter  16:  Penalized Estimation in Complex Models Download PDF
Chapter  17:  High-Dimensional Regression and Inference Download PDF
Chapter  18:  Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation Download PDF
Chapter  19:  Scalable Super Learning Download PDF
Chapter  20:  Tutorial for Causal Inference Download PDF
Chapter  21:  A Review of Some Recent Advances in Causal Inference Download PDF
Chapter  22:  Targeted Learning for Variable Importance Download PDF
Chapter  23:  Online Estimation of the Average Treatment Effect Download PDF
Chapter  24:  Mining with Inference: Data-Adaptive Target Parameters Download PDF
prelims Download PDF
Index Download PDF
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