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<p>High-dimensional data with complex dependence structures are routinely collected in clinical and social science studies, where leveraging such structure can reveal latent pathways or regulatory networks and improve variable selection performance. In this work, we develop a generalized-distribution-based Bayesian approach for consistent network-guided variable selection in high-dimensional linear regression. Our novel approach simultaneously incorporates hierarchical spike-and-slab priors on the regression coefficients and the elements of the inverse covariance matrix. We further establish strong selection consistency of the proposed methodology and propose a highly scalable Gibbs sampler for posterior computation. Through simulation studies, we demonstrate that our method achieves superior performance relative to state-of-the-art alternatives. We analyze amplitude of low-frequency fluctuation (ALFF) neuroimaging data for individuals with autism spectrum disorder (ASD) to identify key brain regions associated with cognitive performance, providing insight into neural mechanisms related to ASD and benchmarking against existing methods.</p>

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Scalable network-driven variable selection in Bayesian linear regression

  • 期刊论文

  • 发表日期：

    2026年7月18日

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  • Jiarong Ouyang,
  • Beniamino Hadj-Amar,
  • Xuan Cao

摘要

High-dimensional data with complex dependence structures are routinely collected in clinical and social science studies, where leveraging such structure can reveal latent pathways or regulatory networks and improve variable selection performance. In this work, we develop a generalized-distribution-based Bayesian approach for consistent network-guided variable selection in high-dimensional linear regression. Our novel approach simultaneously incorporates hierarchical spike-and-slab priors on the regression coefficients and the elements of the inverse covariance matrix. We further establish strong selection consistency of the proposed methodology and propose a highly scalable Gibbs sampler for posterior computation. Through simulation studies, we demonstrate that our method achieves superior performance relative to state-of-the-art alternatives. We analyze amplitude of low-frequency fluctuation (ALFF) neuroimaging data for individuals with autism spectrum disorder (ASD) to identify key brain regions associated with cognitive performance, providing insight into neural mechanisms related to ASD and benchmarking against existing methods.