On Semiparametric Exponential Family Graphical Models

Understand

We propose a new class of semiparametric exponential family graphical models for the analysis of high dimensional mixed data.

• Different from the existing mixed graphical models, we allow the nodewise conditional distributions to be semiparametric generalized linear models with unspecified base measure functions.
• Thus, one advantage of our method is that it is unnecessary to specify the type of each node and the method is more convenient to apply in practice.
• Under the proposed model, we consider both problems of parameter estimation and hypothesis testing in high dimensions.