Handbook of big data - Boca Raton CRC Press 2016 - xvi, 464 p. - Chapman & Hall/CRC handbooks of modern statistical methods .

"Chapman & Hall book."

Table of Contents


1. GENERAL PERSPECTIVES ON BIG DATA
The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data
Richard Starmans
Big n versus Big p in Big Data
Norman Matloff

2. DATA-CENTRIC, EXPLORATORY METHODS
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

3. EFFICIENT ALGORITHMS
High-Dimensional Computational Geometry
Alexandr Andoni
IRLBA: Fast Partial SVD 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

4. GRAPH APPROACHES
Networks
Elizabeth L. Ogburn and Alexander Volfovsky
Mining Large Graphs
David F. Gleich and Michael W. Mahoney

5. MODEL FITTING AND REGULARIZATION
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 Models
Jacob Bien and Daniela Witten
High-Dimensional Regression and Inference
Lukas Meier

6. ENSEMBLE METHODS
Divide and Recombine Subsemble, Exploiting the Power of Cross-Validation
Stephanie Sapp and Erin LeDell
Scalable Super Learning
Erin LeDell

7. CAUSAL INFERENCE
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

8. TARGETED LEARNING
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





Features

Depicts the current landscape of big data analysis
Emphasizes computational statistics and machine learning
Strikes the right balance not only between statistical theory and applications in computer science but also between the breadth of topics and the depth to which each topic is explored

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.

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.


https://www.crcpress.com/Handbook-of-Big-Data/Buhlmann-Drineas-Kane-van-der-Laan/p/book/9781482249071

9781482249071


Big data
Statistical methods
Handbooks - Manuals

005.7 / H2

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