Graphical Models of Autoregressive Moving-Average Processes
2010 (English)In: The 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2010), 2010Conference paper (Refereed)
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
IdentifiersURN: urn:nbn:se:kth:diva-55900OAI: oai:DiVA.org:kth-55900DiVA: diva2:472072
The 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2010), Budapest, Hungary, 5-9 July2010
FunderSwedish Research Council
QC 201201172012-01-172012-01-032013-09-05Bibliographically approved