Umfang:
1 Online-Ressource (XIV, 115 p. 26 illus. in color.)
ISBN:
9783030800659
Serie:
SpringerBriefs in Applied Statistics and Econometrics
Inhalt:
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.
Inhalt:
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.
Weitere Ausg.:
ISBN 9783030800642
Weitere Ausg.:
ISBN 9783030800666
Weitere Ausg.:
Erscheint auch als Druck-Ausgabe ISBN 9783030800642
Weitere Ausg.:
Erscheint auch als Druck-Ausgabe ISBN 9783030800666
Weitere Ausg.:
Erscheint auch als Druck-Ausgabe Zagidullina, Aygul High-dimensional covariance matrix estimation Cham, Switzerland : Springer Nature, 2021 ISBN 9783030800642
Sprache:
Englisch
DOI:
10.1007/978-3-030-80065-9
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