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High-Dimensional Covariance Matrix Estimation An Introduction to Random Matrix Theory
Author
Title
High-Dimensional Covariance Matrix Estimation [electronic resource] : An Introduction to Random Matrix Theory / by Aygul Zagidullina.
ISBN
9783030800659
Edition
1st ed. 2021.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2021.
Physical Description
1 online resource (XIV, 115 p.) 26 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
November 03, 2021
Series
SpringerBriefs in Applied Statistics and Econometrics,
Contents
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.
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SpringerLink (Online service)
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