书目名称 | High-Dimensional Covariance Matrix Estimation |
副标题 | An Introduction to R |
编辑 | Aygul Zagidullina |
视频video | |
概述 | Presents random matrix theory and covariance matrix estimation under high-dimensional asymptotics.Demonstrates the deficiencies of the standard statistical tools when applied in high dimensions.Encour |
丛书名称 | SpringerBriefs in Applied Statistics and Econometrics |
图书封面 |  |
描述 | 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. |
出版日期 | Book 2021 |
关键词 | covariance matrix estimation; random matrix theory; high-dimensional asymptotics; high-dimensional cova |
版次 | 1 |
doi | https://doi.org/10.1007/978-3-030-80065-9 |
isbn_softcover | 978-3-030-80064-2 |
isbn_ebook | 978-3-030-80065-9Series ISSN 2524-4116 Series E-ISSN 2524-4124 |
issn_series | 2524-4116 |
copyright | The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 |