Exact confidence regions for linear regression parameter under external arbitrary noise

A. Senov, K. Amelin, N. Amelina, O. Granichin

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearch

7 Scopus citations

Abstract

The paper propose new method for identifying non-asymptotic confidence regions for linear regression parameter under external arbitrary noise. This method called Modified Sign-Perturbed Sums (MSPS) method and it is a modification of previously proposed one, called Sign-Perturbed Sums which is applicable only in case of symmetrical centred noise. MSPS algorithm correctness and obtained confidence region convergence are proved theoretically under some additional assumptions. SPS and MSPS methods are compared basing on simulated data. Few advantages of MSPS method in case of biased and asymmetric noise are illustrated.
Original languageEnglish
Title of host publicationIn: Proc. of the 2014 American Control Conference (ACC),
Pages5097-5102
DOIs
StatePublished - 2014

Keywords

  • Linear systems
  • Randomized algorithms
  • Uncertain systems

Fingerprint

Dive into the research topics of 'Exact confidence regions for linear regression parameter under external arbitrary noise'. Together they form a unique fingerprint.

Cite this