High-Dimensional Covariance Matrix Estimation - An Introduction to Random Matrix Theory (Paperback, 1st ed. 2021)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Applied Statistics and Econometrics
Release date:	October 2021
First published:	2021
Authors:	Aygul Zagidullina
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	115
Edition:	1st ed. 2021
ISBN-13:	978-3-03-080064-2
Languages:	English
Subtitles:	English
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Business & Economics > Economics > Econometrics > General Books > Computing & IT > Applications of computing > Databases > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-03-080064-4
Barcode:	9783030800642

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