SpringerBriefs in Applied Statistics and Econometrics: An Introduction to Random Matrix Theory

Zagidullina, Aygul

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.

Details

Published by: Springer

Publication Date: 2021-10-30

Format: Paperback

ISBN-13: 9783030800642

DOI: 10.1007/978-3-030-80065-9

Dimensions: 235cm x155cm

Pages: 115