论文标题
融合数据集成中的平均结构学习与依赖性
Fused mean structure learning in data integration with dependence
论文作者
论文摘要
由图像量表回归的动机，与多个站点之间的数据汇总，我们考虑了一个设置，其中多个独立研究收集了多个依赖性矢量结果，并在研究和结果向量之间具有潜在的平均模型参数均匀性。为了确定共同分析这些数据源的有效性，我们必须了解这些数据源共享平均模型参数。我们提出了一种新的模型融合方法，可在现有方法上提供提高的灵活性，统计性能和计算速度。我们提出的方法指定了每个数据源中的二次推理函数，并根据成对融合惩罚的新公式将平均模型参数向量融合在其整体中。我们建立了估计器的理论特性，并提出了一个渐变的加权甲骨文元估计器，该估计量在计算上更有效。模拟和应用于遵守神经感染联盟的应用突出了所提出的方法的灵活性。提供了R软件包，以便于实施。
Motivated by image-on-scalar regression with data aggregated across multiple sites, we consider a setting in which multiple independent studies each collect multiple dependent vector outcomes, with potential mean model parameter homogeneity between studies and outcome vectors. To determine the validity of jointly analyzing these data sources, we must learn which of these data sources share mean model parameters. We propose a new model fusion approach that delivers improved flexibility, statistical performance and computational speed over existing methods. Our proposed approach specifies a quadratic inference function within each data source and fuses mean model parameter vectors in their entirety based on a new formulation of a pairwise fusion penalty. We establish theoretical properties of our estimator and propose an asymptotically equivalent weighted oracle meta-estimator that is more computationally efficient. Simulations and application to the ABIDE neuroimaging consortium highlight the flexibility of the proposed approach. An R package is provided for ease of implementation.
