High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory
High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory by Aygul Zagidullina
English | PDF,EPUB | 2021 | 123 Pages | ISBN : 3030800644 | 16.2 MB
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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