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Bayesian Dynamic Generalized Linear Models in Non-Conjugated Models
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Mojdeh Esmailzadeh *   , Farzad Eskandari , Sima Naghizadeh Ardabili |
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Abstract: (19235 Views) |
| Forecasting the future status for underlying systems or random process, is one of the most important problems. In such situations, in addition to variables, the parameters may vary during the time and hence, the independence assumption between variables and parameters is broken. For analyzing this systems, usually the dynamic generalized linear models are used based on Markov chain Monte Carlo algorithm. The purpose of this paper is applying the Bayesian dynamic generalized linear models in non-conjugate discrete structures. First, the concepts of dynamic generalized linear models are reviewed. Then, the Bayesian modeling of non-conjugated discrete structures using MCMC algorithm is studied. Finally, using the investigated model the real data set related to the economic activity condition in three provinces of Iran during the years 2006-2008 are analysed. |
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| Keywords: Autoregressive process, Bayesian dynamic generalized linear models, Non-conjugated discrete structures |
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Full-Text [PDF 1014 kb]
(3562 Downloads)
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Type of Study: Applied |
Subject:
Applied Statistics
Received: 2012/03/10 | Accepted: 2013/05/14 | Published: 2013/05/14
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