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Graphical Models of Autoregressive Moving-Average Processes
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-2681-8383
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-1927-1690
2010 (English)In: The 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2010), 2010Conference paper, Published paper (Refereed)
Abstract [en]

Consider a Gaussian stationary stochastic vector process with the property that designated pairs of components are conditionally independent given the rest of the components. Such processes can be represented on a graph where the components are nodes and the lack of a connecting link between two nodes signifies conditional independence. This leads to a sparsity pattern in the inverse of the matrix-valued spectral density. Such graphical models find applications in speech, bioinformatics, image processing, econometrics and many other fields, where the problem to fit an autoregressive (AR) model to such a process has been considered. In this paper we take this problem one step further, namely to fit an autoregressive moving-average (ARMA) model to the same data. We develop a theoretical framework which also spreads further light on previous approaches and results.

Place, publisher, year, edition, pages
2010.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-55900OAI: oai:DiVA.org:kth-55900DiVA: diva2:472072
Conference
The 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2010), Budapest, Hungary, 5-9 July2010
Funder
Swedish Research Council
Note
QC 20120117Available from: 2012-01-17 Created: 2012-01-03 Last updated: 2013-09-05Bibliographically approved

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IR-EE-RT 2010:032(191 kB)340 downloads
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Lindquist, AndersWahlberg, Bo

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CiteExportLink to record
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