Missing observation analysis for matrix-variate time series data

Details Missing observation analysis for matrix-variate time series data Missing observation analysis for matrix-variate time series data

2008-05-25

arxiv.org/abs/0805.3831v1

Statistics and Probability Letters (2008), 78, pp. 2647-2653.

Statistics

Methodology

11 pages, 1 figure

Scientific paper

10.1016/j.spl.2008.03.033

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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