High-Dimensional Covariance Estimation: With High-Dimensional Data
Mohsen Pourahmadi
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 ... Read more
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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About Mohsen Pourahmadi
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