High-Dimensional Covariance Estimation

With High-Dimensional Data

Autor: Mohsen Pourahmadi

Jazyk:

Vazba: Pevná

Vydavatel: John Wiley & Sons Inc

Dostupnost: Skladem u dodavatele

Odesíláme za 14-21 dnů

2 207 Kč

Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play...

Informace o knize

Autor

Mohsen Pourahmadi

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

2013

Stránek

208

EAN

9781118034293

ISBN

1118034295

Enbook ID

02406506

Vydavatel

John Wiley & Sons Inc

Hmotnost

470

Rozměry

240 x 160 x 18

Kompletní popis

Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and Regularization Regularizing the Eigenstructure Banding, Tapering, and Thresholding Covariance Matrices Sparse Gaussian Graphical Models Multivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.

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With High-Dimensional Data

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