Triantafyllopoulos, K. (2008) Missing observation analysis for matrix-variate time series data. Statistics and Probability Letters, 78 (16). pp. 2647-2653. ISSN 0167-7152Full text available as:
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.
|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:||23 Jun 2014 08:14|