ARMA Identification of Graphical Models
2013 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 58, no 5, 1167-1178 p.Article in journal (Refereed) Published
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 and an optimization procedure which also spreads further light on previous approaches and results. This procedure is then applied to the identification problem of estimating the ARMA parameters as well as the topology of the graph from statistical data.
Place, publisher, year, edition, pages
2013. Vol. 58, no 5, 1167-1178 p.
Autoregressive moving-average (ARMA) modeling, conditional independence, graphical models, system identification
IdentifiersURN: urn:nbn:se:kth:diva-39065DOI: 10.1109/TAC.2012.2231551ISI: 000318542200006ScopusID: 2-s2.0-84886418860OAI: oai:DiVA.org:kth-39065DiVA: diva2:439404
FunderSwedish Research Council
Updated from "Preprint" to "Article" QC 201306272011-09-082011-09-072013-09-05Bibliographically approved