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1. |
Record Nr. |
UNISA990002823220203316 |
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Titolo |
5. : Legazioni di Romagna, 1512-1699 / introduzione di Gianni Guadalupi |
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Pubbl/distr/stampa |
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Milano, : F. M. Ricci, copyr. 2001 |
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ISBN |
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Descrizione fisica |
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Collana |
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Disciplina |
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Soggetti |
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Stato della Chiesa - Sec. 15.-17 |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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2. |
Record Nr. |
UNINA9910508455703321 |
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Autore |
Zagidullina Aygul |
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Titolo |
High-Dimensional Covariance Matrix Estimation : An Introduction to Random Matrix Theory / / by Aygul Zagidullina |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (123 pages) |
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Collana |
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SpringerBriefs in Applied Statistics and Econometrics, , 2524-4124 |
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Disciplina |
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Soggetti |
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Statistics |
Econometrics |
Big data |
Machine learning |
Statistics in Business, Management, Economics, Finance, Insurance |
Big Data |
Statistical Theory and Methods |
Machine Learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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, |
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econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work. |
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