Librería Portfolio

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.



Table of Contents

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

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

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

GRAPH APPROACHES

Networks
Elizabeth L. Ogburn and Alexander Volfovsky

Mining Large Graphs
David F. Gleich and Michael W. Mahoney

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

ENSEMBLE METHODS

Divide and Recombine Subsemble, Exploiting the Power of Cross-Validation
Stephanie Sapp and Erin LeDell

Scalable Super Learning
Erin LeDell

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

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