Abstract In this paper, we propose a new approach of innovated scalable dynamic learning (ISDL) for estimating time-varying graphical structures. Motivated by the innovated transformation, we convert the original problem into large covariance matrix estimation and exploit the scaled Lasso with kernel smoothing to simplify the tuning procedure. In addition, we show that our method has theoretical guarantees under mild regularity conditions for accurate estimation of each precision matrix.

中文翻译：

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用于时变图形模型的创新可扩展动态学习

摘要 在本文中，我们提出了一种用于估计时变图形结构的创新可扩展动态学习 (ISDL) 新方法。受创新变换的启发，我们将原始问题转换为大协方差矩阵估计，并利用具有核平滑的缩放套索来简化调整过程。此外，我们表明我们的方法在温和的规律性条件下具有理论保证，可以准确估计每个精度矩阵。