High-Dimensional Statistics: A Non-Asymptotic Viewpoint

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ID: 288153
2019
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Abstract
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.
Reference Key
persistent_1761420398_68fd246e608c0 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Martin J. Wainwright
Journal ADVANCES IN ARCHAEOLOGICAL PRACTICE
Year 2019
DOI
10.1017/9781108627771
URL
https://www.cambridge.org/core/books/highdimensional-statistics/8A91ECEEC38F46DAB53E9FF8757C7A4E
Keywords Keywords not found

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