High-dimensional covariance matrix estimation : an introduction to random matrix theory / Aygul Zagidullina.

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High-dimensional covariance matrix estimation : an introduction to random matrix theory / Aygul Zagidullina.

9783030800659 (electronic bk.)
3030800652 (electronic bk.)
9783030800642
3030800644

Cham : Springer, [2021]

©2021

English

1 online resource : color illustrations.

10.1007/978-3-030-80065-9 doi

QA276.8 .Z34 2021

519.5/44

This book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.

Includes bibliographical references.

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Online resource; title from PDF title page (SpringerLink, viewed November 15, 2021).

SpringerBriefs in applied statistics and econometrics.

Print version: 9783030800642

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