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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory

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

Zagidullina, Aygul

Paperback

Series: Springerbriefs in Applied Statistics and Econometrics

Business GeneralEconomicsDatabases

ISBN10: 3030800644
ISBN13: 9783030800642
Publisher: Springer Nature
Published: Oct 30 2021
Pages: 115
Weight: 0.43
Height: 0.28 Width: 6.14 Depth: 9.21
Language: English
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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