By Mohsen Pourahmadi

Methods for estimating sparse and massive covariance matrices

Covariance and correlation matrices play basic roles in each element of the research of multivariate information amassed from various fields together with company and economics, future health care, engineering, and environmental and actual sciences. High-Dimensional Covariance Estimation provides obtainable and entire insurance of the classical and sleek ways for estimating covariance matrices in addition to their purposes to the speedily constructing parts mendacity on the intersection of information and desktop learning.

Recently, the classical pattern covariance methodologies were converted and more desirable upon to satisfy the wishes of statisticians and researchers facing huge correlated datasets. High-Dimensional Covariance Estimation specializes in the methodologies according to shrinkage, thresholding, and penalized chance with functions to Gaussian graphical types, prediction, and mean-variance portfolio administration. The publication is predicated seriously on regression-based principles and interpretations to attach and unify many current equipment and algorithms for the task.

High-Dimensional Covariance Estimation positive factors chapters on:

  • Data, Sparsity, and Regularization
  • Regularizing the Eigenstructure
  • Banding, Tapering, and Thresholding
  • Covariance Matrices
  • Sparse Gaussian Graphical Models
  • Multivariate Regression

The booklet is a perfect source for researchers in information, arithmetic, company and economics, laptop sciences, and engineering, in addition to an invaluable textual content or complement for graduate-level classes in multivariate research, covariance estimation, statistical studying, and high-dimensional info analysis.

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