Quotation Frühwirth-Schnatter, Sylvia. 1994. Applied state space modelling of non-Gaussian timeseries using integration-based Kalman filtering. Statistics and Computing. 4 259-269.


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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.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Statistics and Computing
Citation Index SCI
WU-Journal-Rating new FIN-A, VW-D
Language English
Title Applied state space modelling of non-Gaussian timeseries using integration-based Kalman filtering
Volume 4
Year 1994
Page from 259
Page to 269
URL http://www.springerlink.com/content/l142074573333172/
DOI http://dx.doi.org/10.1007/BF00156749
Open Access N

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Frühwirth-Schnatter, Sylvia (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
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