Bayesian Model Selection via Composite Likelihood for High-dimensional Data Integration

Abstract

We consider data integration problems where correlated data are collected from multiple platforms. Within each platform, there are linear relationships between the responses and a collection of predictors. We extend the linear models to include random errors coming from a much wider family of sub-Gaussian and subexponential distributions. The goal is to select important predictors across multiple platforms, where the number of predictors and the number of observations both increase to infinity. We combine the marginal densities of the responses obtained from different platforms to form a composite likelihood and propose a model selection criterion based on Bayesian composite posterior probabilities. Under some regularity conditions, we prove that the model selection criterion is consistent to recover the union support of the predictors with divergent true model size.