Overview
- Presents random matrix theory and covariance matrix estimation under high-dimensional asymptotics
- Demonstrates the deficiencies of the standard statistical tools when applied in high dimensions
- Encourages practitioners to use the new techniques when dealing with big data problems
Part of the book series: SpringerBriefs in Applied Statistics and Econometrics (SBASE)
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Table of contents (5 chapters)
Keywords
- covariance matrix estimation
- random matrix theory
- high-dimensional asymptotics
- high-dimensional covariance matrix estimation
- sample covariance matrix estimator
- statistical inference
- big data
- high-dimensional statistics
- shrinkage estimation of covariance matrices
- linear spectral statistics for high-dimensional inference
About this book
Authors and Affiliations
About the author
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.
Bibliographic Information
Book Title: High-Dimensional Covariance Matrix Estimation
Book Subtitle: An Introduction to Random Matrix Theory
Authors: Aygul Zagidullina
Series Title: SpringerBriefs in Applied Statistics and Econometrics
DOI: https://doi.org/10.1007/978-3-030-80065-9
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021
Softcover ISBN: 978-3-030-80064-2Published: 30 October 2021
eBook ISBN: 978-3-030-80065-9Published: 29 October 2021
Series ISSN: 2524-4116
Series E-ISSN: 2524-4124
Edition Number: 1
Number of Pages: XIV, 115
Number of Illustrations: 26 illustrations in colour
Additional Information: 1st edition originally published electronically on KOPS of the University of Konstanz, 2019
Topics: Statistics for Business, Management, Economics, Finance, Insurance, Econometrics, Big Data, Statistical Theory and Methods, Machine Learning