Non-asymptotic Confidence Regions for the Transfer Functions of Errors-in-Variables Systems

Abstract

Finite-sample system identification (FSID) methods provide guaranteed confidence regions for the unknown model parameter of dynamical systems under mild statistical assumptions for a finite number of data points. In this article, two FSID methods, the leave-out sign-dominant correlation region (LSCR) and sign-perturbed sums (SPS) methods are extended to errors-in-variables (EIV) systems. In EIV systems, both the measured input and the measured output are corrupted by noise, and they are not noise invertible in the sense that the noise signals cannot be recovered from the measured signals given the true system. Hence, standard FSID methods are not applicable. The present article deals with FS..