High-Dimensional Covariance Matrix Estimation / SpringerBriefs in Applied Statistics and Econometrics (PDF)
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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.
- Autor: Aygul Zagidullina
- 2021, 1st ed. 2021, 115 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3030800652
- ISBN-13: 9783030800659
- Erscheinungsdatum: 29.10.2021
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- Größe: 5.20 MB
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