Håkan Hjalmarsson (KTH Royal Institute of Technology)
Minimizing the Mean-Square-Error of Identified Finite Impulse Response Models
In this presentation we discuss methods for minimizing the mean-squared error of identified finite impulse response models. In particular we consider regularized models and cast the choice of regularization parameter as a direct optimization problem. Several approaches to this problem are considered, including classical SURE (Stein’s Unbiased Risk Estimation) and weighted least-squares. The results are compared with current state-of-the art kernel methods.