High-Dimensional Statistics: A Non-Asymptotic Viewpoint

Cambridge Series in Statistical and Probabilistic Mathematics #48

by Martin J. Wainwright

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.

Genres: Mathematics, Textbooks, Research

568 pages, Hardcover

Published April 11, 2019

Reviews

[Matthew Zhang · Rating 5 out of 5]

Very clear book!