High-Dimensional Covariance Matrix Estimation : An Introduction to Random Matrix Theory / / by Aygul Zagidullina

• Autore: Zagidullina Aygul
• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
• Edizione: 1st ed. 2021.
• Descrizione fisica: 1 online resource (123 pages)
• Disciplina: 512.9434
• Soggetto topico: Statistics; Econometrics; Big data; Machine learning; Statistics in Business, Management, Economics, Finance, Insurance; Big Data; Statistical Theory and Methods; Machine Learning
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: High-Dimensional Covariance Matrix Estimation
• ISBN: 9783030800659; 3030800652
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910508455703321
• Lo trovi qui: Univ. Federico II
• Serie: SpringerBriefs in Applied Statistics and Econometrics, . 2524-4124