Bayesian Graphical Regression

Yang Ni, Francesco C. Stingo, Veerabhadran Baladandayuthapani

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

We consider the problem of modeling conditional independence structures in heterogenous data in the presence of additional subject-level covariates—termed graphical regression. We propose a novel specification of a conditional (in)dependence function of covariates—which 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.

Original languageEnglish (US)
Pages (from-to)184-197
Number of pages14
JournalJournal of the American Statistical Association
Volume114
Issue number525
DOIs
StatePublished - Jan 2 2019

Keywords

  • Directed acyclic graph
  • Nonlocal prior
  • Predictive network
  • Subject-specific graph
  • Varying graph structure

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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