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nexusstc/High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory/0df282675f4c791be7066a8942c348ed.pdf
High-Dimensional Covariance Matrix Estimation : An Introduction to Random Matrix Theory 🔍
Aygul Zagidullina Springer International Publishing : Imprint: Springer, SpringerBriefs in Applied Statistics and Econometrics, SpringerBriefs in Applied Statistics and Econometrics, 1, 2021
英语 [en] · PDF · 5.5MB · 2021 · 📗 未知类型的图书 · nexusstc · Save
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.
Erscheinungsdatum: 30.10.2021
备选作者
Zagidullina, Aygul
备用出版商
Springer International Publishing AG
备用出版商
Springer Nature Switzerland AG
备用出版商
University of Konstanz
备用版本
SpringerBriefs in Applied Statistics and Econometrics Ser, Cham, 2022
备用版本
SpringerBriefs in applied statistics and econometrics, Cham, 2021
备用版本
Springer Nature, Cham, 2021
备用版本
1st ed. 2021, Cham, 2021
备用版本
Switzerland, Switzerland
备用版本
1st ed, S.l, 2021
元数据中的注释
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