High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory (SpringerBriefs in Applied Statistics and Econometrics) - Softcover

Zagidullina, Aygul

Softcover

ISBN 10: 3030800644 ISBN 13: 9783030800642

Verlag: Springer, 2021

Inhaltsangabe

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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Über die Autorin bzw. den Autor

Aygul Zagidullina received her Ph.D. in Quantitative Economics and Finance from the University of Konstanz, Germany, with a specialization in the areas of financial econometrics and statistical modeling. Her research interests include estimation of high-dimensional covariance matrices, machine learning, factor models and neural networks.

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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.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2021
• Sprache: Englisch
• ISBN 10: 3030800644
• ISBN 13: 9783030800642
• Einband: Taschenbuch
• Auflage: 1
• Anzahl der Seiten: 132
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