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Approximative covariance interpolation with a quadratic penalty
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2007 (English)In: Proceedings Of The 46th IEEE Conference On Decision And Control, Vols 1-14, 2007, 4489-4494 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.
Series
IEEE Conference on Decision and Control, ISSN 0191-2216
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-41050ISI: 000255181702133Scopus ID: 2-s2.0-62749156803ISBN: 978-1-4244-1497-0 (print)OAI: oai:DiVA.org:kth-41050DiVA: diva2:444800
Conference
46th IEEE Conference on Decision and Control Location: New Orleans, LA Date: DEC 12-14, 2007
Note
QC 20110930Available from: 2011-09-30 Created: 2011-09-23 Last updated: 2011-09-30Bibliographically approved

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