Resource title

Data Augmentation and Dynamic Linear Models

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Resource description

We define a subclass of dynamic linear models with unknown hyperparameters called d-inverse-gamma models. We then approximate the marginal p.d.f.s of the hyperparameter and the state vector by the data augmentation algorithm of Tanner/Wong. We prove that the regularity conditions for convergence hold. A sampling based scheme for practical implementation is discussed. Finally, we illustrate how to obtain an iterative importance sampling estimate of the model likelihood. (author's abstract) ; Series: Forschungsberichte / Institut für Statistik

Resource author

Sylvia Frühwirth-Schnatter

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Resource language

en

Resource content type

application/pdf

Resource resource URL

http://epub.wu.ac.at/392/1/document.pdf

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