High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory (1st ed. 2021) (SpringerBriefs in Applied Statistics and Econometrics)
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- Synopsis
- 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.
- Copyright:
- 2021
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9783030800659
- Related ISBNs:
- 9783030800642
- Publisher:
- Springer International Publishing
- Date of Addition:
- 10/29/21
- Copyrighted By:
- The Author
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Computers and Internet, Business and Finance, Mathematics and Statistics
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
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