← 研踪 PaperTrailarXiv:2403.12357 · v2
Unifying Sparse Estimation of Precision and Covariance Matrices
首次提交 2024-03-19 10:01
版本更新 2026-10-27 04:59
雷达观测 2026-10-29 01:07
摘要
Penalized maximum likelihood is a widely used approach to estimate a sparse precision or a sparse covariance matrix. A zero in the precision matrix encodes a conditional independence and a zero in the covariance matrix a marginal independence, and the two estimators have been developed separately, each with its own algorithm. We show that these two problems can be unified via a single reparametrization of the last column of either matrix. This reparametrization decouples the likelihood function for each problem to a one-dimensional minimization followed by a penalized multiple regression. Our approach applies to any penalty that is separable in the off-diagonal entries and linear on the diagonal. When the penalty is the $\ell_1$ norm, it unifies the graphical Lasso and the covariance graphical Lasso algorithms.
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v2 · 2026-10-27 04:59 · 最新观测版本
Unifying Sparse Estimation of Precision and Covariance Matrices
Penalized maximum likelihood is a widely used approach to estimate a sparse precision or a sparse covariance matrix. A zero in the precision matrix encodes a conditional independence and a zero in the covariance matrix a marginal independence, and the two estimators have been developed separately, each with its own algorithm. We show that these two problems can be unified via a single reparametrization of the last column of either matrix. This reparametrization decouples the likelihood function for each problem to a one-dimensional minimization followed by a penalized multiple regression. Our approach applies to any penalty that is separable in the off-diagonal entries and linear on the diagonal. When the penalty is the $\ell_1$ norm, it unifies the graphical Lasso and the covariance graphical Lasso algorithms.
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