Missing observation analysis for matrix-variate time series data

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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Year of publication:	2008
Authors:	Triantafyllopoulos, K.
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 78.2008, 16, p. 2647-2653
Publisher:	Elsevier

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005254528