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The covariance matrix is a fundamental quantity that helps us understand the nature of relationships among variables in a multivariate data set. Estimating the covariance matrix can be challenging in… Click to show full abstract
The covariance matrix is a fundamental quantity that helps us understand the nature of relationships among variables in a multivariate data set. Estimating the covariance matrix can be challenging in modern applications where the number of variables is often larger than the number of samples. In this paper, we review methods which tackle this challenge by inducing sparsity in the Cholesky parameter of the inverse covariance matrix.
Keywords:
estimation gaussian;
dimensional covariance;
covariance;
covariance matrix;
high dimensional;
covariance estimation
Journal Title: Wiley Interdisciplinary Reviews: Computational Statistics
Year Published: 2019
Link to full text (if available)
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Journal Title: Wiley Interdisciplinary Reviews: Computational Statistics
Year Published: 2019
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!