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Highdimensional Statistics A Nonasymptotic Viewpoint Wainwright

SKU: BELL-9971860

$ 31.00 ~~$ 45.00~~ (-31%)

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Highdimensional Statistics A Nonasymptotic Viewpoint Wainwright instant download after payment.

Publisher: Cambridge University Press

File Extension: PDF

File size: 3.75 MB

Pages: 552

Author: Wainwright, Martin J

ISBN: 9781108498029, 9781108627771, 1108498027, 1108627773

Language: English

Year: 2019

Product desciption

Highdimensional Statistics A Nonasymptotic Viewpoint Wainwright by Wainwright, Martin J 9781108498029, 9781108627771, 1108498027, 1108627773 instant download after payment.

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.
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