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Bayesian analysis of time series Poisson data

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  • Man-Suk Oh
  • Yong Bin Lim

Abstract

This paper provides a practical simulation-based Bayesian analysis of parameter-driven models for time series Poisson data with the AR(1) latent process. The posterior distribution is simulated by a Gibbs sampling algorithm. Full conditional posterior distributions of unknown variables in the model are given in convenient forms for the Gibbs sampling algorithm. The case with missing observations is also discussed. The methods are applied to real polio data from 1970 to 1983.

Suggested Citation

  • Man-Suk Oh & Yong Bin Lim, 2001. "Bayesian analysis of time series Poisson data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(2), pages 259-271.
    • Handle: RePEc:taf:japsta:v:28:y:2001:i:2:p:259-271
    DOI: 10.1080/02664760020016154
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    References listed on IDEAS

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    1. Oh, Man-Suk, 1999. "Estimation of posterior density functions from a posterior sample," Computational Statistics & Data Analysis, Elsevier, vol. 29(4), pages 411-427, February.
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    Cited by:

    1. Oh, Man-Suk, 2014. "Bayesian test on equality of score parameters in the order restricted RC association model," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 147-157.

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