High-Dimensional Covariance Matrix Estimation
An Introduction to Random Matrix Theory
Seiten
2021
|
1st ed. 2021
Springer International Publishing (Verlag)
978-3-030-80064-2 (ISBN)
Springer International Publishing (Verlag)
978-3-030-80064-2 (ISBN)
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 |
| Maße | 155 x 235 mm |
| Gewicht | 213 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik |
| 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 |
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