Bayes linear covariance matrix adjustment for multivariate dynamic linear models

dc.creatorWilkinson, Darren J
dc.creatorGoldstein, Michael
dc.date1995-06-05
dc.date.accessioned2026-07-07T09:07:33Z
dc.date.available2026-07-07T09:07:33Z
dc.descriptionA methodology is developed for the adjustment of the covariance matrices underlying a multivariate constant time series dynamic linear model. The covariance matrices are embedded in a distribution-free inner-product space of matrix objects which facilitates such adjustment. This approach helps to make the analysis simple, tractable and robust. To illustrate the methods, a simple model is developed for a time series representing sales of certain brands of a product from a cash-and-carry depot. The covariance structure underlying the model is revised, and the benefits of this revision on first order inferences are then examined.
dc.descriptionIn submission. LaTeX, 17 pages, Chicago BIB-style (included). Also available as a postscript file from http://fourier.dur.ac.uk:8000/~dma3djw/djwgdlm.html For information about [B/D], go to http://fourier.dur.ac.uk:8000/stats/bd/
dc.identifierhttps://arxiv.org/abs/bayes-an/9506002
dc.identifierhttp://arxiv.org/abs/bayes-an/9506002
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/150411
dc.subjectData Analysis, Statistics and Probability
dc.titleBayes linear covariance matrix adjustment for multivariate dynamic linear models
dc.typetext

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