Skip to main content
Article thumbnail
Location of Repository

Missing observation analysis for matrix-variate time series data

By K. Triantafyllopoulos

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.\ud \u

Publisher: Elsevier
Year: 2008
OAI identifier: oai:eprints.whiterose.ac.uk:10625

Suggested articles


To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.