Structural breaks detection and variable selection in dynamic linear regression via the Iterative Fused LASSO in high dimension
高维动态线性回归中基于迭代融合 Lasso 的结构突变检测与变量选择
We aim to develop a time series modeling methodology tailored to high-dimensional environments, addressing two critical challenges: variable selection from a large pool of candidates, and the detection of structural break points, where the model's parameters shift. This effort centers on formulating a least squares estimation problem with regularization constraints, drawing on techniques such as Fused LASSO and AdaLASSO, which are well-established in machine learning. Our…