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Titlebook: High-Dimensional Covariance Matrix Estimation; An Introduction to R Aygul Zagidullina Book 2021 The Author(s), under exclusive licence to S

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发表于 2025-3-21 18:03:45 | 显示全部楼层 |阅读模式
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Titlebook: High-Dimensional Covariance Matrix Estimation; An Introduction to R Aygul Zagidullina Book 2021 The Author(s), under exclusive licence to S
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
doihttps://doi.org/10.1007/978-3-030-80065-9
isbn_softcover978-3-030-80064-2
isbn_ebook978-3-030-80065-9Series ISSN 2524-4116 Series E-ISSN 2524-4124
issn_series 2524-4116
copyrightThe Author(s), under exclusive licence to Springer Nature Switzerland AG 2021
The information of publication is updating

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发表于 2025-3-21 23:18:45 | 显示全部楼层
2524-4116 ard statistical tools when applied in high dimensions.EncourThis 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 tradit
发表于 2025-3-22 03:32:45 | 显示全部楼层
Introduction,m matrix theory perspective. This alternative framework provides powerful tools that enable the analysis of random matrices (and sample covariance matrix, in particular) stemming from the data that researchers and practitioners currently encounter.
发表于 2025-3-22 06:20:03 | 显示全部楼层
Book 2021d 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.
发表于 2025-3-22 09:14:18 | 显示全部楼层
Aygul Zagidullinaience problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time ser
发表于 2025-3-22 14:15:32 | 显示全部楼层
Aygul Zagidullinaience problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time ser
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发表于 2025-3-23 03:48:26 | 显示全部楼层
Aygul Zagidullinay morphological and biochemical changes in most cell compartments (Kerr et al., 1972; Wyllie et al., 1980). The microscopically observed nuclear alterations such as marginatiom and condensation of chromatin and nuclear fragmentation are accompanied by sequential degradation of the DNA, first into fr
发表于 2025-3-23 07:35:44 | 显示全部楼层
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