标题： 多受试者高维稀疏向量自回归模型的联合建模与推断 显示英文标题

标题： Joint modeling and inference of multiple-subject high-dimensional sparse vector autoregressive models

摘要： 多变量向量自回归（multi-VAR）模型通过将个体稀疏VAR转移矩阵分解为共同共享和主题独特路径，捕捉了跨主题的异质网络Granger因果关系。 该模型已被用于表征主题之间的隐藏共享和独特路径，并与心理学和神经科学中常用的方法相比展示了性能。 尽管有这一创新，该模型由于使用加权中位数来识别共同效应，导致统计效率低下，因为共同路径和独特路径的收敛速度由最不稀疏的主题和所有主题中最小的样本量决定。 我们基于一种通信高效的数据显示集成框架，提出了一个多-VAR模型的新可识别性条件。 我们证明了这种方法实现了针对每个主题的稀疏水平和样本量的收敛速度。 此外，我们开发了假设检验来评估个体路径的零性和同质性，使用从个体去偏估计量构建的Wald型检验统计量。 也可以通过该框架推导出共同路径显著性的检验。 在各种异质性场景下的模拟研究和一个实际数据应用表明，与现有基准相比，所提出的方法在标准评估指标上的表现良好。 显示英文摘要

摘要： The multiple-subject vector autoregression (multi-VAR) model captures heterogeneous network Granger causality across subjects by decomposing individual sparse VAR transition matrices into commonly shared and subject-unique paths. The model has been applied to characterize hidden shared and unique paths among subjects and has demonstrated performance compared to methods commonly used in psychology and neuroscience. Despite this innovation, the model suffers from using a weighted median for identifying the common effects, leading to statistical inefficiency as the convergence rates of the common and unique paths are determined by the least sparse subject and the smallest sample size across all subjects. We propose a new identifiability condition for the multi-VAR model based on a communication-efficient data integration framework. We show that this approach achieves convergence rates tailored to each subject's sparsity level and sample size. Furthermore, we develop hypothesis tests to assess the nullity and homogeneity of individual paths, using Wald-type test statistics constructed from individual debiased estimators. A test for the significance of the common paths can also be derived through the framework. Simulation studies under various heterogeneity scenarios and a real data application demonstrate the performance of the proposed method compared to existing benchmark across standard evaluation metrics.