Integration-based Kalman-filtering for a Dynamic Generalized Linear Trend Model

Schnatter, Sylvia (1991) Integration-based Kalman-filtering for a Dynamic Generalized Linear Trend Model. Forschungsberichte / Institut für Statistik, 9. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna.


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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. (author's abstract)

Item Type: Paper
Keywords: Approximate Bayesian Inference / Bayesian Computation / Dynamic Generalized Linear Models / Gauss-Hermite Integration / Kalman-Filtering for Non-Gaussian Data / Non-Gaussian State Space Models / Trend Modelling
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 11 Jul 2006 08:16
Last Modified: 22 Oct 2019 00:41


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