Triantafyllopoulos, K. (2009) Inference of dynamic generalized linear models: on-line computation and appraisal. International Statistical Review, 77 (3). pp. 430-450. ISSN 0306-7734Full text not available from this repository. (Request a copy)
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.
|Keywords:||Dynamic generalized linear model; Bayesian forecasting; sequential Monte Carlo; particle filters; non-Gaussian time series; state space; Kalman filter|
|Academic Units:||The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield)
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|Depositing User:||Mrs Megan Hobbs|
|Date Deposited:||22 Mar 2010 15:55|
|Last Modified:||07 Jun 2010 11:10|
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