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

Zagidullina, Aygul

Couverture souple

ISBN 10 : 3030800644 ISBN 13 : 9783030800642

Editeur : Springer, 2021

Synopsis

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.

A propos du livre

À propos de l'auteur

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.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2021
• Langue: anglais
• ISBN 10: 3030800644
• ISBN 13: 9783030800642
• Reliure: Broché
• Numéro d'édition: 1
• Nombre de pages: 132
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne