A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees

20 Jul 2013 Kshitij Khare Sang-Yun Oh Bala Rajaratnam

Sparse high dimensional graphical model selection is a topic of much interest in modern day statistics. A popular approach is to apply l1-penalties to either (1) parametric likelihoods, or, (2) regularized regression/pseudo-likelihoods, with the latter having the distinct advantage that they do not explicitly assume Gaussianity... (read more)

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