paper

Missing observation analysis for matrix-variate time series data

arXiv:0805.3831 · doi:10.1016/j.spl.2008.03.033

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 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.

11 pages, 1 figure

Missing observation analysis for matrix-variate time series data · wovepaper