Barbosa, Emanuel Pimentel (1989) Dynamic Bayesian models for vector time series analysis & forecasting. PhD thesis, University of Warwick.

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Abstract

This thesis considers the Bayesian analysis of general multivariate DLM's (Dynamic Linear
Models) for vector time series forecasting where the observational variance matrices are
unknown. This extends considerably some previous work based on conjugate analysis for
a special sub—class of vector DLM's where all marginal univariate models follow the same
structure.
The new methods developed in this thesis, are shown to have a better performance than
other competing approaches to vector DLM analysis, as for instance, the one based on the
Student t filter.
Practical aspects of implementation of the new methods, as well as some theoretical properties
are discussed, further model extensions are considered, including non—linear models
and some applications with real and simulated data are provided.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	Q Science > QA Mathematics
Library of Congress Subject Headings (LCSH):	Bayesian statistical decision theory, Multivariate analysis, Linear models (Statistics), Time-series analysis
Official Date:	November 1989
Dates:	Date Event November 1989 Submitted
Institution:	University of Warwick
Theses Department:	Department of Statistics
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Harrison, Jeff
Sponsors:	Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ; Universidade Federal de São Carlos
Extent:	v, 184 leaves
Language:	eng

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