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[ subject:"Asymptotic efficiencies (Statistics)" ]
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High-dimensional covariance matrix e...
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Zagidullina, Aygul.
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High-dimensional covariance matrix estimation = an introduction to random matrix theory /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
High-dimensional covariance matrix estimation/ by Aygul Zagidullina.
其他題名:
an introduction to random matrix theory /
作者:
Zagidullina, Aygul.
出版者:
Cham :Springer International Publishing : : 2021.,
面頁冊數:
xiv, 115 p. :ill., digital ;24 cm.
內容註:
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.
Contained By:
Springer Nature eBook
標題:
Random matrices. -
電子資源:
https://doi.org/10.1007/978-3-030-80065-9
ISBN:
9783030800659
High-dimensional covariance matrix estimation = an introduction to random matrix theory /
Zagidullina, Aygul.
High-dimensional covariance matrix estimation
an introduction to random matrix theory /[electronic resource] :by Aygul Zagidullina. - Cham :Springer International Publishing :2021. - xiv, 115 p. :ill., digital ;24 cm. - SpringerBriefs in applied statistics and econometrics,2524-4124. - SpringerBriefs in applied statistics and econometrics..
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.
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.
ISBN: 9783030800659
Standard No.: 10.1007/978-3-030-80065-9doiSubjects--Topical Terms:
646309
Random matrices.
LC Class. No.: QA276.8 / .Z34 2021
Dewey Class. No.: 519.544
High-dimensional covariance matrix estimation = an introduction to random matrix theory /
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