Data augmentation and dynamic linear models

Publication: Scientific journalJournal articlepeer-review


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.
Original languageEnglish
Pages (from-to)183 - 202
JournalJournal of Time Series Analysis
Publication statusPublished - 1994

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