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 …

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 …

Abstract Sign-Perturbed-Sums (SPS) is a system identification algorithm that, under mild
assumptions on the distribution of the noise, constructs confidence regions with finite …

Sign-Perturbed Sums (SPS) is a recently developed non-asymptotic system identification
algorithm that constructs confidence regions for parameters of dynamical systems. It works …

Sign-Perturbed Sums (SPS) is a recently developed finite sample system identification
method that can build exact confidence regions for linear regression problems under mild …

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 …

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 …

In parameter estimation, it is often desirable to supplement the estimates with an
assessment of their quality. A new family of methods proposed by Campi et al. for this …

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 …

We introduce a general testing procedure in models with possible identification failure that
has exact asymptotic rejection probability under the null hypothesis. The procedure is widely …