A proximal distance algorithm for likelihood-based sparse covariance estimation

Estimating large correlation matrices for international migration

Jason Xu and Kenneth Lange

Biometrika, 2022, vol. 109, issue 4, 1047-1066

Abstract: SummaryThis paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes the distance from the covariance estimate to a symmetric sparsity set. This formulation avoids unwanted shrinkage induced by more common norm penalties, and enables optimization of the resulting nonconvex objective by solving a sequence of smooth, unconstrained subproblems. These subproblems are generated and solved via the proximal distance version of the majorization-minimization principle. The resulting algorithm executes rapidly, gracefully handles settings where the number of parameters exceeds the number of cases, yields a positive-definite solution, and enjoys desirable convergence properties. Empirically, we demonstrate that our approach outperforms competing methods across several metrics, for a suite of simulated experiments. Its merits are illustrated on international migration data and a case study on flow cytometry. Our findings suggest that the marginal and conditional dependency networks for the cell signalling data are more similar than previously concluded.

Keywords: Distance-to-set penalty; Majorization-minimization; Penalized likelihood; Proximal algorithm; Sequential unconstrained minimization; Sparse estimation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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