| Autore: |
Zagidullina, Aygul
|
| Titolo: |
High-Dimensional Covariance Matrix Estimation : An Introduction to Random Matrix Theory / Aygul Zagidullina
|
| Pubblicazione: |
Cham, : Springer, 2021 |
| Descrizione fisica: |
xiv, 115 p. : ill. ; 24 cm |
| Soggetto topico: |
60-XX - Probability theory and stochastic processes [MSC 2020] |
| |
60B20 - Random matrices (probabilistic aspects) [MSC 2020] |
| |
62-XX - Statistics [MSC 2020] |
| |
62H12 - Estimation in multivariate analysis [MSC 2020] |
| |
62J10 - Analysis of variance and covariance (ANOVA) [MSC 2020] |
| |
62P20 - Applications of statistics to economics [MSC 2020] |
| |
62R07 - Statistical aspects of big data and data science [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.: | VAN00274816 |
|
| Lo trovi qui: | Univ. Vanvitelli |
| Localizzazioni e accesso elettronico |
https://doi.org/10.1007/978-3-030-80065-9 |
| Opac: |
Controlla la disponibilità qui |
