Missing observation analysis for matrix-variate time series data

K. Triantafyllopoulos

doi:10.1016/j.spl.2008.03.033

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arXiv · 0805.3831

Missing observation analysis for matrix-variate time series data

K. Triantafyllopoulos

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Abstract

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.

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K. Triantafyllopoulos. 2008-05-25. Missing observation analysis for matrix-variate time series data. https://doi.org/10.1016/j.spl.2008.03.033

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