Bayes linear covariance matrix adjustment for multivariate dynamic linear models

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A 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.
In 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/

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Data Analysis, Statistics and Probability

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http://salesiana.dossiersoluciones.com/handle/123456789/150411

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/bayes-an/9506002
 http://arxiv.org/abs/bayes-an/9506002

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arXiv