High-Dimensional Covariance Matrix Estimation : An Introduction to Random Matrix Theory

1st ed. 2021

by Aygul Zagidullina

Book Details

Format Paperback / Softback

Book Series SpringerBriefs in Applied Statistics and Econometrics

ISBN-10 3030800644

ISBN-13 9783030800642

Edition 1st ed. 2021

Publisher Springer Nature Switzerland AG

Imprint Springer Nature Switzerland AG

Country of Manufacture GB

Country of Publication GB

Publication Date Oct 30th, 2021

Print length 115 Pages

Product Classification: Econometrics Probability & statistics Databases Machine learning

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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.

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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