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

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

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 integ...

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Bibliographic Details
Main Author: Schnatter, Sylvia
Format: Others
Language:en
Published: Department of Statistics and Mathematics, WU Vienna University of Economics and Business 1991
Subjects:
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
Online Access:http://epub.wu.ac.at/424/1/document.pdf

Internet

http://epub.wu.ac.at/424/1/document.pdf

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