High-Dimensional Covariance Matrix Estimation

High-Dimensional Covariance Matrix Estimation
Author :
Publisher : Springer Nature
Total Pages : 123
Release :
ISBN-10 : 9783030800659
ISBN-13 : 3030800652
Rating : 4/5 (652 Downloads)

Book Synopsis High-Dimensional Covariance Matrix Estimation by : Aygul Zagidullina

Download or read book High-Dimensional Covariance Matrix Estimation written by Aygul Zagidullina and published by Springer Nature. This book was released on 2021-10-29 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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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