Abstract
This paper presents a novel algorithm for constructing non-asymptotic confidence ellipsoids for linear regression models with deterministic regressors. The confidence ellipsoids are centered at the least-squares estimate, and the true parameter lies within the ellipsoid with a probability specified by the user. The confidence ellipsoid is constructed by drawing independent samples of the noise sequence and exploiting an ordering property for independent and identically distributed random variables. The main assumption on the noise is that it has a known joint density so that samples can be drawn. The efficacy of the approach is demonstrated through a simulation example of an FIR system. The constructed confidence ellipsoid is compared with the confidence regions obtained using the Sign-Perturbed Sum method and asymptotic system identification theory.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
