Applied State Space Modelling of Non-Gaussian Time Series using Integration-based Kalman-filtering

Publication: Working/Discussion PaperWU Working Paper

Abstract

The main topic of the paper is on-line filtering for non-Gaussian dynamic (state space) models by approximate computation of the first two posterior moments using efficient numerical integration. Based on approximating the prior of the state vector by a normal density, we prove that the posterior moments of the state vector are related to the posterior moments of the linear predictor in a simple way. For the linear predictor Gauss-Hermite integration is carried out with automatic reparametrization based on an approximate posterior mode filter. We illustrate how further topics in applied state space modelling such as estimating hyperparameters, computing model likelihoods and predictive residuals, are managed by integration-based Kalman-filtering. The methodology derived in the paper is applied to on-line monitoring of ecological time series and filtering for small count data. (author's abstract)
Original languageEnglish
Place of PublicationVienna
PublisherDepartment of Statistics and Mathematics, WU Vienna University of Economics and Business
Publication statusPublished - 1993

Publication series

NameForschungsberichte / Institut für Statistik
No.35

WU Working Paper Series

  • Forschungsberichte / Institut für Statistik

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