Abstract Sign-Perturbed Sums (SPS) is a system identification method that constructs non-
asymptotic confidence regions for the parameters of linear regression models under mild …

We propose a new system identification method, called Sign-Perturbed Sums (SPS), for
constructing non-asymptotic confidence regions under mild statistical assumptions. SPS is …

In this paper we propose an algorithm for constructing non-asymptotic confidence regions
for parameters of general linear systems under mild statistical assumptions. The constructed …

Abstract Sign-Perturbed Sums (SPS) is a finite sample system identification method that
constructs exact, non-asymptotic confidence regions for the unknown parameters of linear …

Hypothesis testing methods that do not rely on exact distribution assumptions have been
emerging lately. The method of sign-perturbed sums (SPS) is capable of characterizing …

One of the central issues in system identification consists not only in obtaining a good model
of the process under study but also an informative confidence interval around it. This …

In this paper we consider the problem of constructing non-asymptotic confidence regions for
the parameters of Errors-In-Variables (EIV) systems where both inputs and outputs are …

In this paper we consider the problem of constructing confidence regions for the parameters
of identified models of dynamical systems. Taking a major departure from the previous …

We propose a generalization of the recently developed system identification method called
Sign-Perturbed Sums (SPS). The proposed construction is based on the instrumental …

The paper propose new method for identifying non-asymptotic confidence regions for linear
regression parameter under external arbitrary noise. This method called Modified Sign …