High-Dimensional Covariance Matrix Estimation : An Introduction to Random Matrix Theory / by Aygul Zagidullina.

Format:

Book

Author/Creator:

Zagidullina, Aygul, author.

Series:

SpringerBriefs in Applied Statistics and Econometrics, 2524-4124

Language:

English

Subjects (All):

Statistics.

Econometrics.

Big data.

Machine learning.

Statistics in Business, Management, Economics, Finance, Insurance.

Big Data.

Statistical Theory and Methods.

Machine Learning.

Local Subjects:

Statistics in Business, Management, Economics, Finance, Insurance.

Econometrics.

Big Data.

Statistical Theory and Methods.

Machine Learning.

Physical Description:

1 online resource (123 pages)

Edition:

1st ed. 2021.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

Summary:

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.

Contents:

Foreword

1 Introduction

2 Traditional Estimators and Standard Asymptotics

3 Finite Sample Performance of Traditional Estimators

4 Traditional Estimators and High-Dimensional Asymptotics

5 Summary and Outlook

Appendices.

ISBN:

9783030800659

3030800652

OCLC:

1286428618