Integration-based Kalman filtering for a dynamic generalized linear trend model

Publication: Scientific journalJournal articlepeer-review

Abstract

The topic of the paper is filtering for non-Gaussian dynamic (state space) models by approximate computation of posterior moments using numerical integration. A Gauss-Hermite procedure is implemented based on the approximate posterior mode estimator and curvature recently proposed in 121. This integration-based filtering method will be illustrated by a dynamic trend model for non-Gaussian time series. Comparision of the proposed method with other approximations ([15], [2]) is carried out by simulation experiments for time series from Poisson, exponential and Gamma distributions.
Original languageEnglish
Pages (from-to)447 - 459
JournalComputational Statistics and Data Analysis
Volume13
Publication statusPublished - 1 Oct 1992

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