| Dewey Class |
300.727 |
| Titolo |
High-Dimensional Covariance Matrix Estimation ([EBook] :) : An Introduction to Random Matrix Theory / / by Aygul Zagidullina. |
| Autore |
Zagidullina, Aygul |
| Other name(s) |
SpringerLink (Online service) |
| Edition statement |
1st ed. 2021. |
| Pubblicazione |
Cham : : Springer International Publishing : : Imprint: Springer, , 2021. |
| Physical Details |
XIV, 115 p. 26 illus. in color. : online resource. |
| Serie |
SpringerBriefs in Applied Statistics and Econometrics 2524-4124 |
| ISBN |
9783030800659 |
| Summary Note |
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 note |
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. |
| Mode of acces to digital resource |
Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format. |
| System details note |
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| Internet Site |
https://doi.org/10.1007/978-3-030-80065-9 |
| Link alle Opere Legate |
Riferimenti soggetto: .
Big Data .
Econometrics .
Machine Learning .
Statistical Theory and Methods .
Statistics .
Statistics in Business, Management, Economics, Finance, Insurance .
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