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High-dimensional covariance matrix e...

Zagidullina, Aygul.

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High-dimensional covariance matrix estimation = an introduction to random matrix theory /

Record Type:	Electronic resources : Monograph/item
Title/Author:	High-dimensional covariance matrix estimation/ by Aygul Zagidullina.
Reminder of title:	an introduction to random matrix theory /
Author:	Zagidullina, Aygul.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xiv, 115 p. :ill., digital ;24 cm.
[NT 15003449]:	Foreword -- 1 Introduction -- 2 Traditional Estimators and Standard Asymptotics -- 3 Finite Sample Performance of Traditional Estimators -- 4 Traditional Estimators and High-Dimensional Asymptotics -- 5 Summary and Outlook -- Appendices.
Contained By:	Springer Nature eBook
Subject:	Random matrices. -
Online resource:	https://doi.org/10.1007/978-3-030-80065-9
ISBN:	9783030800659

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

High-dimensional covariance matrix estimationan introduction to random matrix theory /[electronic resource] :by Aygul Zagidullina. - Cham :Springer International Publishing :2021. - xiv, 115 p. :ill., digital ;24 cm. - SpringerBriefs in applied statistics and econometrics,2524-4124. - SpringerBriefs in applied statistics and econometrics..

Foreword -- 1 Introduction -- 2 Traditional Estimators and Standard Asymptotics -- 3 Finite Sample Performance of Traditional Estimators -- 4 Traditional Estimators and High-Dimensional Asymptotics -- 5 Summary and Outlook -- Appendices.

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.

ISBN: 9783030800659

Standard No.: 10.1007/978-3-030-80065-9doiSubjects--Topical Terms:

646309
Random matrices.

LC Class. No.: QA276.8 / .Z34 2021

Dewey Class. No.: 519.544

High-dimensional covariance matrix estimation = an introduction to random matrix theory /
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245 1 0 $a High-dimensional covariance matrix estimation $h [electronic resource] : $b an introduction to random matrix theory / $c by Aygul Zagidullina.
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300 $a xiv, 115 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied statistics and econometrics, $x 2524-4124
505 0 $a Foreword -- 1 Introduction -- 2 Traditional Estimators and Standard Asymptotics -- 3 Finite Sample Performance of Traditional Estimators -- 4 Traditional Estimators and High-Dimensional Asymptotics -- 5 Summary and Outlook -- Appendices.
520 $a 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.
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