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- W799765830 abstract "This thesis considers the Bayesian analysis of general multivariate DLM's (Dynamic LinearModels) for vector time series forecasting where the observational variance matrices areunknown. This extends considerably some previous work based on conjugate analysis fora special sub—class of vector DLM's where all marginal univariate models follow the samestructure.The new methods developed in this thesis, are shown to have a better performance thanother competing approaches to vector DLM analysis, as for instance, the one based on theStudent t filter.Practical aspects of implementation of the new methods, as well as some theoretical propertiesare discussed, further model extensions are considered, including non—linear modelsand some applications with real and simulated data are provided." @default.
- W799765830 created "2016-06-24" @default.
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- W799765830 date "1989-11-01" @default.
- W799765830 modified "2023-09-23" @default.
- W799765830 title "Dynamic Bayesian models for vector time series analysis & forecasting" @default.
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