The Library
Dynamic Bayesian models for vector time series analysis & forecasting
Tools
Barbosa, Emanuel Pimentel (1989) Dynamic Bayesian models for vector time series analysis & forecasting. PhD thesis, University of Warwick.
|
PDF
WRAP_THESIS_Barbosa_1989.pdf - Submitted Version - Requires a PDF viewer. Download (7Mb) |
Official URL: http://webcat.warwick.ac.uk/record=b1456673~S1
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 (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: |
|
||||
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Publication Status: | Unpublished | ||||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 |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year