Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 103, Issue -, Pages 284-303Publisher
ELSEVIER
DOI: 10.1016/j.csda.2016.05.012
Keywords
Graphical modeling; High-dimensional precision matrix estimation; Multivariate normal l(2)-penalization; Precision matrix
Funding
- European Community's Seventh Framework Programme (FP7) [FP7-269553]
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The ridge estimation of the precision matrix is investigated in the setting where the number of variables is large relative to the sample size. First, two archetypal ridge estimators are reviewed and it is noted that their penalties do not coincide with common quadratic ridge penalties. Subsequently, starting from a proper l(2)-penalty, analytic expressions are derived for two alternative ridge estimators of the precision matrix. The alternative estimators are compared to the archetypes with regard to eigenvalue shrinkage and risk. The alternatives are also compared to the graphical lasso within the context of graphical modeling. The comparisons may give reason to prefer the proposed alternative estimators. (C) 2016 Elsevier B.V. All rights reserved.
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