“Multiview Data” is a term used to describe heterogeneous data measured on the same set of observations but collected from different sources and of potentially different types (continuous, discrete, count). This type of data is prevalent in varied areas such as imaging genetics, national security, social networking, Internet advertising, and our particular motivation - high-throughput integrative genomics. There have been limited efforts at statistically modeling such mixed data jointly. Recently, new Mixed Markov Random Field (MRFs) distributions, or graphical models, were proposed that assume each node-conditional
distribution arises from a different exponential family model. These yield joint densities, which can directly parameterize dependencies over mixed variables. Fitting these models to perform mixed graph selection entails estimating penalized generalized linear models with mixed covariates. Model selection with mixed covariates in high dimensional setting, however, poses many challenges due to differences in the scale and potential signal interference between variables. In this thesis, we introduce this novel class of MRFs, study model estimation challenges theoretically and empirically, and propose a new iterative block estimation strategy. Our methods are applied to infer a gene regulatory network that integrates methylation, small RNA expression, mutation, and gene expression data to fully understand regulatory relationships in ovarian cancer.