High Dimensional Covariance Matrix Estimation
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High-Dimensional Covariance Matrix Estimation
Author | : Aygul Zagidullina |
Publisher | : Springer Nature |
Total Pages | : 123 |
Release | : 2021-10-29 |
Genre | : Business & Economics |
ISBN | : 3030800652 |
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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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