Missing observation analysis for matrix-variate time series data

Simple item page
dc.creatorTriantafyllopoulos, K.
dc.date2008-05-25
dc.date.accessioned2026-07-07T12:34:12Z
dc.date.available2026-07-07T12:34:12Z
dc.descriptionBayesian 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.
dc.description11 pages, 1 figure
dc.identifierhttps://arxiv.org/abs/0805.3831
dc.identifierhttp://arxiv.org/abs/0805.3831
dc.identifierStatistics and Probability Letters (2008), 78, pp. 2647-2653.
dc.identifierdoi:10.1016/j.spl.2008.03.033
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/217345
dc.subjectMethodology
dc.subjectApplications
dc.titleMissing observation analysis for matrix-variate time series data
dc.typetext

Files

Collections

arXiv