Journal
INTERNATIONAL STATISTICAL REVIEW
Volume 77, Issue 3, Pages 430-450Publisher
INT STATISTICAL INST
DOI: 10.1111/j.1751-5823.2009.00087.x
Keywords
Dynamic generalized linear model; Bayesian forecasting; sequential Monte Carlo; particle filters; non-Gaussian time series; state space; Kalman filter
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P>The purpose of this paper is to provide a critical discussion on real-time estimation of dynamic generalized linear models. We describe and contrast three estimation schemes, the first of which is based on conjugate analysis and linear Bayes methods, the second based on posterior mode estimation, and the third based on sequential Monte Carlo sampling methods, also known as particle filters. For the first scheme, we give a summary of inference components, such as prior/posterior and forecast densities, for the most common response distributions. Considering data of arrivals of tourists in Cyprus, we illustrate the Poisson model, providing a comparative analysis of the above three schemes.
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