High-Dimensional Covariance Estimation
Sparse and structured covariance, graphical lasso, and shrinkage.
High-Dimensional Covariance Estimation. Sparse and structured covariance, graphical lasso, and shrinkage.
Recent technical contributions
A handful of recent papers carry the methodological frontier of high-dimensional covariance estimation forward. Operator norm consistent estimation of large-dimensional sparse covariance matrices (Bickel et al., 2008) is a primary reference for this area and develops new techniques or results that downstream work builds on.
Open methodological questions for high-dimensional covariance estimation include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
Prerequisites
Sources
- paper · primary · 2008bickel-2008, levina-2008
In context
Where this topic sits in the prerequisite graph. Click any node to jump.
Review this topic
This page was drafted by an agent and is waiting on expert review. Spotted a wrong prerequisite, a missing concept, a misattributed source, or a factual slip? Tell us — your review opens a tracked issue maintainers act on.