Innovated scalable dynamic learning for time-varying graphical models

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.