Téléchargez le livre : High-Dimensional Covariance Matrix Estimation

High-Dimensional Covariance Matrix Estimation

An Introduction to Random Matrix Theory

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Éditeur :

Springer


Paru le : 2021-10-29



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Description
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.
Pages
115 pages
Collection
n.c
Parution
2021-10-29
Marque
Springer
EAN papier
9783030800642
EAN PDF
9783030800659
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
11
Taille du fichier
5458 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
73,84 €
EAN EPUB
9783030800659
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
11
Taille du fichier
11264 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
73,84 €

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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