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

• Autore: Zagidullina, Aygul
• Pubblicazione: Cham, : Springer, 2021
• Descrizione fisica: xiv, 115 p. : ill. ; 24 cm
• Soggetto topico: 60-XX - Probability theory and stochastic processes [MSC 2020]; 62H12 - Estimation in multivariate analysis [MSC 2020]; 62-XX - Statistics [MSC 2020]; 62P20 - Applications of statistics to economics [MSC 2020]; 62R07 - Statistical aspects of big data and data science [MSC 2020]; 62J10 - Analysis of variance and covariance (ANOVA) [MSC 2020]; 60B20 - Random matrices (probabilistic aspects) [MSC 2020]
• Soggetto non controllato: Big Data; Covariance matrix estimation; High-dimensional asymptotics; High-dimensional covariance matrix estimation; High-dimensional statistics; Linear spectral statistics for high-dimensional inference; Random matrix theory; Sample covariance matrix estimator; Shrinkage estimation of covariance matrices; Statistical inference
• Titolo autorizzato: High-Dimensional Covariance Matrix Estimation
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: VAN0274816
• Lo trovi qui: Univ. Vanvitelli
• Localizzazioni e accesso elettronico: https://doi.org/10.1007/978-3-030-80065-9
• Serie: SpringerBriefs in Applied Statistics and Econometrics Berlin [etc.] . -Springer