High-Dimensional Mixed Graphical Models Estimation
Description
Provides weighted lasso framework for high-dimensional mixed data graph estimation.
In the graph estimation stage, the graph structure is estimated by maximizing the conditional
likelihood of one variable given the rest. We focus on the conditional loglikelihood of each variable
and fit separate regressions to estimate the parameters, much in the spirit of the neighborhood
selection approach proposed by Meinshausen-Buhlmann for the Gaussian Graphical Model and by Ravikumar
for the Ising Model. Currently, the discrete variables can only take two values. In the future, method
for general discrete data and for visualizing the estimated graph will be added.
For more details, see the linked paper.