High-Dimensional Covariance Matrix Estimation
Produktnummer:
18f4daafb7cc904fde8e302b88e55ae578
| Autor: | Zagidullina, Aygul |
|---|---|
| Themengebiete: | big data covariance matrix estimation high-dimensional asymptotics high-dimensional covariance matrix estimation high-dimensional statistics linear spectral statistics for high-dimensional inference random matrix theory sample covariance matrix estimator shrinkage estimation of covariance matrices statistical inference |
| Veröffentlichungsdatum: | 29.10.2021 |
| EAN: | 9783030800659 |
| Sprache: | Englisch |
| Seitenzahl: | 115 |
| Produktart: | Unbekannt |
| Verlag: | Springer International Publishing |
| Untertitel: | An Introduction to Random Matrix Theory |
Produktinformationen "High-Dimensional Covariance Matrix Estimation"
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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