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Missing observation analysis for matrix-variate time series data

Triantafyllopoulos, K. (2008) Missing observation analysis for matrix-variate time series data. Statistics and Probability Letters, 78 (16). pp. 2647-2653. ISSN 0167-7152

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Bayesian inference is developed for matrix-variate dynamic linear models (MV-DLMs), in order to allow missing observation analysis, of any sub-vector or sub-matrix of the observation time series matrix. We propose modifications of the inverted Wishart and matrix t distributions, replacing the scalar degrees of freedom by a diagonal matrix of degrees of freedom. The MV-DLM is then re-defined and modifications of the updating algorithm for missing observations are suggested.

Item Type: Article
Copyright, Publisher and Additional Information: © 2008 Elsevier. This is an author produced version of a paper subsequently published in Statistics and Probability Letters. Uploaded in accordance with the publisher's self-archiving policy
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: 29 Mar 2010 13:34
Last Modified: 17 Nov 2015 02:17
Published Version: http://dx.doi.org/10.1016/j.spl.2008.03.033
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.spl.2008.03.033
Related URLs:
URI: http://eprints.whiterose.ac.uk/id/eprint/10625

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