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High-Dimensional Statistics : A Non-Asymptotic Viewpoint, Hardback Book


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.


  • Format: Hardback
  • Pages: 568 pages, Worked examples or Exercises; 1 Tables, black and white; 25 Halftones, black and white; 2
  • Publisher: Cambridge University Press
  • Publication Date:
  • Category: Data analysis: general
  • ISBN: 9781108498029



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