High-Dimensional Covariance Matrix Estimation

An Introduction to Random Matrix Theory

AvAygul Zagidullina

Häftad, Engelska, 2021

Del i serien SpringerBriefs in Applied Statistics and Econometrics

743 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2021-10-30
Mått:155 x 235 x 10 mm
Vikt:210 g
Format:Häftad
Språk:Engelska
Serie:SpringerBriefs in Applied Statistics and Econometrics
Antal sidor:115
Förlag:Springer Nature Switzerland AG
ISBN:9783030800642
Originaltitel:Three Essays on Covariance Matrix Estimation and Factor Models in High Dimensions

Utforska kategorier

Mikroekonomi inom Ekonomi och Ledarskap
Systemvetenskap och AI inom Data och IT
Matematisk statistik inom Naturvetenskap och teknik

Mer om författaren

Aygul Zagidullina received her Ph.D. in Quantitative Economics and Finance from the University of Konstanz, Germany, with a specialization in the areas of financial econometrics and statistical modeling. Her research interests include estimation of high-dimensional covariance matrices, machine learning, factor models and neural networks.

Innehållsförteckning

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.