TIME-VARYING ESTIMATION AND DYNAMIC MODEL SELECTION WITH AN APPLICATION OF NETWORK DATA
TIME-VARYING ESTIMATION AND DYNAMIC MODEL SELECTION WITH AN APPLICATION OF NETWORK DATA
复制标题
应用网络数据的时变估计和动态模型选择
DOI：
10.5705/ss.202017.0218
复制
发表时间：
2020-01-01
影响因子：
1.4
通讯作者：
Qu，Annie
中科院分区：
文献类型：
--
作者：
Xue，Lan;Shu，Xinxin;Qu，Annie
在许多生物医学和社会科学研究中，识别和预测网络数据之间的关联随时间的动态变化是很重要的。我们提出了一个变系数模型，将随时间变化的网络数据，并施加分段惩罚函数，以捕捉网络关联的局部特征。所提出的方法的优点是，它是半参数，因此灵活的建模动态变化的关联网络数据问题，并能够识别的时间区域时，发生动态变化的关联。为了实现网络估计在局部时间间隔的稀疏性，我们实现了一个组惩罚策略，涉及不同的组之间的重叠参数。然而，这给处理在许多时间点观察到的大维度网络数据的优化过程带来了巨大的挑战。我们开发了一个快速算法，平滑邻近梯度法的基础上，这是计算效率和准确性。我们说明了所提出的方法，通过模拟研究和儿童的注意缺陷多动障碍fMRI数据，并表明所提出的方法和算法有效地恢复动态网络随时间的变化。
In many biomedical and social science studies it is important to identify and predict the dynamic changes of associations among network data over time. We propose a varying-coefficient model to incorporate time-varying network data, and impose a piecewise-penalty function to capture local features of the network associations. The advantages of the proposed approach are that it is semi-parametric and therefore flexible in modeling dynamic changes of association for network data problems, and capable of identifying the time regions when dynamic changes of associations occur. To achieve sparsity of network estimation at local time intervals, we implement a group penalization strategy involving overlapping parameters among different groups. However, this imposes great challenges in the optimization process for handling large-dimensional network data observed at many time points. We develop a fast algorithm, based on the smoothing proximal gradient method, which is computationally efficient and accurate. We illustrate the proposed method through simulation studies and children’s attention deficit hyperactivity disorder fMRI data, and show that the proposed method and algorithm efficiently recover dynamic network changes over time.