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High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory

by Aygul Zagidullina
Save 35% Save 35%
Current price ₹5,087.00
Original price ₹7,825.00
Original price ₹7,825.00
Original price ₹7,825.00
(-35%)
₹5,087.00
Current price ₹5,087.00

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Book cover type: Paperback
  • ISBN13: 9783030800642
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Publication Date:
  • Pages: 115
  • Original Price: EUR 69.99
  • Language: English
  • Edition: 2021
  • Item Weight: 211 grams
  • BISAC Subject(s): Statistics, Econometrics, and Database Administration & Management

From the Back Cover

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.

Aygul Zagidullina received her Ph.D. in Quantitative Economics and Finance from the University of Konstanz, Germany, with a specialization in the areas of financial econometrics and statistical modeling. Her research interests include estimation of high-dimensional covariance matrices, machine learning, factor models and neural networks.


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