论文标题
一致且可扩展的贝叶斯关节变量和用于疾病诊断功能性脑网络的图形选择
Consistent and scalable Bayesian joint variable and graph selection for disease diagnosis leveraging functional brain network
论文作者
论文摘要
我们考虑回归系数的联合推断和高维概率回归中协变量的逆协方差矩阵，其中预测因子都与二进制响应相关且在功能上相关。在回归系数上具有尖峰和平板先验的分层模型，以及逆协方差矩阵中的元素同时执行可变和图形选择。当允许预测因子的尺寸生长大得多时，我们建立了变量和基础图的联合选择一致性，这是贝叶斯文献中的第一个理论结果。与其他最新方法相比，在高维模拟研究中得出了可伸缩的Gibbs采样器，该采样器在高维模拟研究中的性能更好。我们通过功能性MRI数据集说明了所提出方法的实际影响和实用性，在该数据集中，通过更改功能活动的感兴趣区域和基本功能性脑网络都被推断并集成在一起，以分层疾病的风险。
We consider the joint inference of regression coefficients and the inverse covariance matrix for covariates in high-dimensional probit regression, where the predictors are both relevant to the binary response and functionally related to one another. A hierarchical model with spike and slab priors over regression coefficients and the elements in the inverse covariance matrix is employed to simultaneously perform variable and graph selection. We establish joint selection consistency for both the variable and the underlying graph when the dimension of predictors is allowed to grow much larger than the sample size, which is the first theoretical result in the Bayesian literature. A scalable Gibbs sampler is derived that performs better in high-dimensional simulation studies compared with other state-of-art methods. We illustrate the practical impact and utilities of the proposed method via a functional MRI dataset, where both the regions of interest with altered functional activities and the underlying functional brain network are inferred and integrated together for stratifying disease risk.
