Shrinking characteristics of precision matrix estimators

Abstract

We propose a framework to shrink a user-specified characteristic of a precision matrix estimator that is needed to fit a predictive model. Estimators in our framework minimize the Gaussian negative loglikelihood plus an $L_1$ penalty on a linear or affine function evaluated at the optimization variable corresponding to the precision matrix. We establish convergence rate bounds for these estimators and propose an alternating direction method of multipliers algorithm for their computation. Our simulation studies show that our estimators can perform better than competitors when they are used to fit predictive models. In particular, we illustrate cases where our precision matrix estimators perform worse at estimating the population precision matrix but better at prediction.

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• Regularized estimation of large covariance matrices
• Sparse permutation invariant covariance estimation
• Network exploration via the adaptive LASSO and SCAD penalties
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• Multiclass Sparse Discriminant Analysis
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Cited by in corpus (2)

• A Proximal Distance Algorithm for Likelihood-Based Sparse Covariance Estimation
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