High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory (Paperback) (ISBN-13: 9783030800642)

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.

Author: Aygul Zagidullina
ISBN-10: 3030800644
ISBN-13: 9783030800642
Publisher: Springer
Language: English
Published: 10/30/2021
Pages: 115
Format: Paperback
Weight: 0.43lbs
Size: 9.21h x 6.14w x 0.28d