- Publisher: Cambridge University Press
- Online publication date: February 2019
- Print publication year: 2019
- Online ISBN: 9781108627771
- DOI: https://doi.org/10.1017/9781108627771
Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data.
Larry Wasserman - Carnegie Mellon University, Pennsylvania
Trevor Hastie - Stanford University, California
Genevera Allen - William Marsh Rice University, Texas
John Lafferty - Yale University, Connecticut
Jianqing Fan - Princeton University, New Jersey
Peter Bühlmann - Eidgenössische Technische Hochschule Zürich
Francis Bach - INRIA Paris
Peter Bickel - University of California, Berkeley
Fabio Mainardi Source: MAA Reviews
G. Alastair Young Source: International Statistical Review
Pierre Alquier Source: MatSciNet
Po-Ling Lo Source: Bulletin of the American Mathematical Society
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