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Friday, May 31
Machine Learning
Recent Developments on Machine Learning
Fri, May 31, 10:30 AM - 12:05 PM
Regency Ballroom AB

Shrinking Characteristics of Precision Matrix Estimators (305026)

Aaron Molstad, Fred Hutchinson Cancer Research Center 
*Adam J. Rothman, University of Minnesota 

Keywords: covariance estimation, high-dimensional data, multivariate analysis

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 L1 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.