A Convex Pseudolikelihood Framework for High Dimensional Partial Correlation Estimation with Convergence Guarantees
具有收敛保证的高维偏相关估计凸伪似然框架
Summary Sparse high dimensional graphical model selection is a topic of much interest in modern day statistics. A popular approach is to apply l 1-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…