Approximative covariance interpolation with a quadratic penalty
2007 (English)In: Proceedings Of The 46th IEEE Conference On Decision And Control, Vols 1-14, 2007, 4489-4494 p.Conference paper (Refereed)
Given output data of a stationary stochastic process estimates of the covariances parameters can be obtained. These estimates can be used to determine ARMA models to approximatly fit the data by matching the covariances exactly. However, the estimates of the covariances may contain large errors, especially if they are determined from short data sequences, and thus it makes sense to match the covariances only in an approximative way. Here we consider a convex method for solving an approximative covariance interpolation problem while maximizing the entropy and penalize the quadratic deviation from the nominal covariances.
Place, publisher, year, edition, pages
2007. 4489-4494 p.
, IEEE Conference on Decision and Control, ISSN 0191-2216
IdentifiersURN: urn:nbn:se:kth:diva-41050ISI: 000255181702133ScopusID: 2-s2.0-62749156803ISBN: 978-1-4244-1497-0OAI: oai:DiVA.org:kth-41050DiVA: diva2:444800
46th IEEE Conference on Decision and Control Location: New Orleans, LA Date: DEC 12-14, 2007
QC 201109302011-09-302011-09-232011-09-30Bibliographically approved