论文标题
时间变化网络的两种高斯正则化方法
Two Gaussian regularization methods for time-varying networks
论文作者
论文摘要
我们将时间变化的网络数据建模为从多元高斯分布的实现，并具有随着时间的推移而变化的精确矩阵。为了促进参数估计，我们不仅要求在任何给定时间点处的每个精度矩阵都稀疏，而且相邻时间点处的精度矩阵也相似。我们通过分别概括弹性网和融合的套索来通过两种不同的算法来完成此操作。我们的主要重点是有效的计算算法和用于选择调谐参数的便捷自由度公式。我们通过两项模拟研究来说明我们的方法。通过将它们应用于fMRI数据集，我们还检测到健康个体和ADHD患者之间大脑连通性的一些有趣差异。
We model time-varying network data as realizations from multivariate Gaussian distributions with precision matrices that change over time. To facilitate parameter estimation, we require not only that each precision matrix at any given time point be sparse, but also that precision matrices at neighboring time points be similar. We accomplish this with two different algorithms, by generalizing the elastic net and the fused LASSO, respectively. Our main focuses are efficient computational algorithms and convenient degree-of-freedom formulae for choosing tuning parameters. We illustrate our methods with two simulation studies. By applying them to an fMRI data set, we also detect some interesting differences in brain connectivity between healthy individuals and ADHD patients.
