High-Dimensional Covariance Matrix Estimation

An Introduction to Random Matrix Theory

Aygul Zagidullina (Autor)

Buch | Softcover

XIV, 115 Seiten

2021 | 1st ed. 2021
Springer International Publishing (Verlag)
978-3-030-80064-2 (ISBN)

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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.

lt;p>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.

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.

Erscheinungsdatum	31.10.2021
Reihe/Serie	SpringerBriefs in Applied Statistics and Econometrics
Zusatzinfo	XIV, 115 p. 26 illus. in color.
Verlagsort	Cham
Sprache	englisch
Original-Titel	Three Essays on Covariance Matrix Estimation and Factor Models in High Dimensions
Maße	155 x 235 mm
Gewicht	213 g
Themenwelt	Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
Themenwelt	Wirtschaft ► Allgemeines / Lexika
Schlagworte	Big Data • covariance matrix estimation • High-dimensional Asymptotics • high-dimensional covariance matrix estimation • High-Dimensional Statistics • linear spectral statistics for high-dimensional inference • Random Matrix Theory • sample covariance matrix estimator • shrinkage estimation of covariance matrices • Statistical Inference
ISBN-10	3-030-80064-4 / 3030800644
ISBN-13	978-3-030-80064-2 / 9783030800642
Zustand	Neuware