Handbook of Big Data: 1st Edition (e-Book) book cover

Handbook of Big Data

1st Edition

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

Chapman and Hall/CRC

480 pages

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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

Table of Contents

General Perspectives on Big Data. Data-Centric, Exploratory Methods. Efficient Algorithms. Graph Approaches. Model Fitting and Regularization. Ensemble Methods. Causal Inference. Targeted Learning.

About the Editors

Peter Buhlmann is a professor of statistics at ETH Zurich, Switzerland, fellow of the Institute of Mathematical Statistics, elected member of the International Statistical Institute, and co-author of the book titled Statistics for High-Dimensional Data: Methods, Theory and Applications. He was named a Thomson Reuters' 2014 Highly Cited Researcher in mathematics, served on various editorial boards and as editor of the Annals of Statistics, and delivered numerous presentations including a Medallion Lecture at the 2009 Joint Statistical Meetings, a read paper to the Royal Statistical Society in 2010, the 14th Bahadur Memorial Lectures at the University of Chicago, Illinois, USA, and other named lectures.

Petros Drineas is an associate professor in the Computer Science Department at Rensselaer Polytechnic Institute, Troy, New York, USA. He is the recipient of an Outstanding Early Research Award from Rensselaer Polytechnic Institute, an NSF CAREER award, and two fellowships from the European Molecular Biology Organization. He has served as a visiting professor at the US Sandia National Laboratories; visiting fellow at the Institute for Pure and Applied Mathematics, University of California, Los Angeles; long-term visitor at the Simons Institute for the Theory of Computing, University of California, Berkeley; program director in two divisions at the US National Science Foundation; and worked for industrial labs. He is a co-organizer of the series of workshops on Algorithms for Modern Massive Datasets and his research has been featured in numerous popular press articles.

Michael Kane is a member of the research faculty at Yale University, New Haven, Connecticut, USA. He is a winner of the American Statistical Association's Chambers Statistical Software Award for The Bigmemory Project, a set of software libraries that allow the R programming environment to accommodate large datasets for statistical analysis. He

Subject Categories

BISAC Subject Codes/Headings:
BUS061000
BUSINESS & ECONOMICS / Statistics
COM037000
COMPUTERS / Machine Theory
MAT029000
MATHEMATICS / Probability & Statistics / General