Data Augmentation and Dynamic Linear Models

Publikation: Working/Discussion PaperWU Working Paper

6 Downloads (Pure)

Abstract

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)
OriginalspracheEnglisch
ErscheinungsortVienna
HerausgeberDepartment of Statistics and Mathematics, WU Vienna University of Economics and Business
PublikationsstatusVeröffentlicht - 1992

Publikationsreihe

NameForschungsberichte / Institut für Statistik
Nr.28

WU Working Paper Reihe

  • Forschungsberichte / Institut für Statistik

Dieses zitieren