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Reference prior for matrix-variate dynamic linear models

Triantafyllopoulos, K. (2008) Reference prior for matrix-variate dynamic linear models. Communications in Statistics - Theory and Methods, 37 (6). pp. 947-958. ISSN 0361-0926

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Abstract

We develop reference analysis for matrix-variate dynamic models with unknown observation covariance matrices. Bayesian algorithms for forecasting, estimation, and filtering are derived. This work extends the existing theory of reference analysis for univariate dynamic linear models, and thus it proposes a solution to the specification of the prior distributions for a very wide class of time series models. Subclasses of our models include the widely used multivariate and matrix-variate regression models.

Item Type: Article
Keywords: Kalman filtering; Prior distribution; Prior specification; Reference prior; State-space models; Time series
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield)
Depositing User: Mrs Megan Hobbs
Date Deposited: 19 Mar 2010 12:53
Last Modified: 07 Jun 2010 11:09
Published Version: http://dx.doi.org/10.1080/03610920701693868
Status: Published
Publisher: Taylor & Francis
Identification Number: 10.1080/03610920701693868
URI: http://eprints.whiterose.ac.uk/id/eprint/10621

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