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.
Product details
- Hardback | 568 pages
- 183 x 260 x 35mm | 1,200g
- 11 Apr 2019
- CAMBRIDGE UNIVERSITY PRESS
- Cambridge, United Kingdom
- English
- Worked examples or Exercises; 1 Tables, black and white; 25 Halftones, black and white; 24 Line drawings, black and white
- 1108498027
- 9781108498029
- 71,618
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