A convex pseudolikelihood framework for high dimensional partial correlation estimation with convergence guarantees

  title={A convex pseudolikelihood framework for high dimensional partial correlation estimation with convergence guarantees},
  author={Kshitij Khare and Sang-Yun Oh and Bala Rajaratnam},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  • K. Khare, Sang-Yun Oh, B. Rajaratnam
  • Published 20 July 2013
  • Computer Science, Mathematics
  • Journal of the Royal Statistical Society: Series B (Statistical Methodology)
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 parametric likelihoods, or regularized regression/pseudolikelihoods, with the latter having the distinct advantage that they do not explicitly assume Gaussianity. As none of the popular methods proposed for solving pseudolikelihood‐based objective functions have provable convergence guarantees, it is not clear whether corresponding… 
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