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
-Sparse Gaussian Graphical Models
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.
by Henry David Thoreau
by Charles Darwin
by Mohsen Kavehrad, M.I. Sakib Chowdhury, Zhou Zhou
by Mark Twain
by Charles Dickens
by Frederick Douglass
by Alexis de Tocqueville
by Elizabeth Gaskell
by Carlo Collodi
by Joshua Slocum
by James Fenimore Cooper
Sign up for our email newsletter