ENGLISH

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

By Aygul Zagidullina

Book information

Publisher
Springer
Year
2021
ISBN
3030800644, 9783030800642
Language
english
Format
EPUB
Filesize
11 MB (11539938 bytes)
Series
SpringerBriefs in Applied Statistics and Econometrics
Edition
1
Pages
129\0
Time added
2021-10-31 03:27:09

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.

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