High-Dimensional Statistics - A Non-Asymptotic Viewpoint (Hardcover)

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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Series:	Cambridge Series in Statistical and Probabilistic Mathematics
Release date:	February 2019
Authors:	Martin J Wainwright
Dimensions:	260 x 183 x 35mm (L x W x T)
Format:	Hardcover
Pages:	568
ISBN-13:	978-1-108-49802-9
Categories:	Books > Reference & Interdisciplinary > Communication studies > Data analysis Books > Science & Mathematics > Mathematics > Probability & statistics Books > Science & Mathematics > Biology, life sciences > General Books > Computing & IT > Applications of computing > Signal processing Books > Computing & IT > Applications of computing > Pattern recognition Books > Business & Economics > Economics > Econometrics > Economic statistics Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	1-108-49802-7
Barcode:	9781108498029

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