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

Frühwirth-Schnatter, Sylvia (1993) Applied State Space Modelling of Non-Gaussian Time Series using Integration-based Kalman-filtering. Forschungsberichte / Institut für Statistik, 35. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna.


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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)

Item Type: Paper
Keywords: Approximate Bayesian Inference / Bayesian Computation / Dynamic Generalized Linear Models / Gauss-Hermite Integration / Kalman-Filtering / Model likelihood / Non-normal State Space Models / Non-Gaussian Time Series / Robust Filtering
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 11 Jul 2006 09:44
Last Modified: 22 Oct 2019 00:41


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