Aygul zagidullina (24 resultados)

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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

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Paperback. Condición: New. 1st ed. 2021. 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.…

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Paperback. Condición: New.

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Condición: NEW.

Condición: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Paperback. Condición: Brand New. 129 pages. 9.25x6.10x0.31 inches. In Stock.

Kartoniert / Broschiert. Condición: New. Presents random matrix theory and covariance matrix estimation under high-dimensional asymptoticsDemonstrates the deficiencies of the standard statistical tools when applied in high dimensionsEncourages practitioners to use the new techniqu.

Taschenbuch. Condición: Neu. High-Dimensional Covariance Matrix Estimation | An Introduction to Random Matrix Theory | Aygul Zagidullina | Taschenbuch | xiv | Englisch | 2021 | Springer | EAN 9783030800642 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Paperback. Condición: New. 1st ed. 2021. 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.…

Condición: new. Questo è un articolo print on demand.

Paperback. Condición: Brand New. 129 pages. 9.25x6.10x0.31 inches. In Stock. This item is printed on demand.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 132 pp. Englisch.…

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Condición: New. PRINT ON DEMAND.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 132 pp. Englisch.…