Bayesian Graphical Regression

• 成果类型: Article
• 署名作者: Ni, Yang; Stingo, Francesco C.; Baladandayuthapani, Veerabhadran
• 署名单位: University of Texas System; University of Texas Austin; Rice University; University of Texas System; UTMD Anderson Cancer Center; University of Florence
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1389739
• 发表日期: 2019
• 页码: 184-197
• 关键词: nonlocal prior densities; variable-selection; model selection; myeloma; Heterogeneity; INDEPENDENCE; networks; spike

摘要

We consider the problem of modeling conditional independence structures in heterogenous data in the presence of additional subject-level covariatestermed graphical regression. We propose a novel specification of a conditional (in)dependence function of covariateswhich allows the structure of a directed graph to vary flexibly with the covariates; imposes sparsity in both edge and covariate selection; produces both subject-specific and predictive graphs; and is computationally tractable. We provide theoretical justifications of our modeling endeavor, in terms of graphical model selection consistency. We demonstrate the performance of our method through rigorous simulation studies. We illustrate our approach in a cancer genomics-based precision medicine paradigm, where-in we explore gene regulatory networks in multiple myeloma taking prognostic clinical factors into account to obtain both population-level and subject-level gene regulatory networks. Supplementary materials for this article are available online.