Bayesian Statistics Seminar
North Carolina State University
presents
Dr. Ming Yuan
Georgia Tech University
"Model Selection and Estimation in the Gaussian Graphical Model"
ABSTRACT
We propose penalised likelihood methods for estimating the concentration matrix in the Gaussian graphical model. The methods lead to a sparse and shrinkage estimator of the concentration matrix that is positive definite, and thus conduct model selection and estimation simultaneously. The implementation of the methods is nontrivial because of the positive definite constraint on the concentration matrix, but we show that the computation can be done effectively by taking advantage of the efficient maxdet algorithm developed in convex optimisation. We propose a BIC type criterion for the selection of the tuning parameter in the penalised likelihood methods. The connection between our methods and existing methods is illustrated. Simulations and real examples demonstrate the competitive performance of the new methods.
Tuesday, October, 31, 2006
4:00 - 5:00 pm
208 Patterson Hall