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.
Epub (Kobo), Epub (Adobe)
Publication Date: 21/02/2019
- ISBN:
- 9781108571234
- 9781108571234
- Category:
- Probability & statistics
- Format:
- Epub (Kobo), Epub (Adobe)
- Publication Date:
- 21-02-2019
- Language:
- English
- Publisher:
- Cambridge University Press
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