Original paper
Large-scale Sparse Inverse Covariance Matrix Estimation
Volume: 41, Issue: 1, Pages: A380 - A401
Published: Jan 1, 2019
Abstract
The estimation of large sparse inverse covariance matrices is a ubiquitous statistical problem in many application areas such as mathematical finance, geology, health, and many others. The \ell_1regularized Gaussian maximum likelihood (ML) method is a common approach for recovering inverse covariance matrices for datasets with a very limited number of samples. A highly efficient ML-based method is the quadratic approximate inverse covariance...
Paper Details
Title
Large-scale Sparse Inverse Covariance Matrix Estimation
Published Date
Jan 1, 2019
Volume
41
Issue
1
Pages
A380 - A401