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.
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 (, ) is carried out by simulation experiments for time series from Poisson, exponential and Gamma distributions. (author's abstract)