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Bayesian predictive inference for time series count data.

M H Chen1, J G Ibrahim

  • 1Department of Mathematical Sciences, Worcester Polytechnic Institute, Massachusetts 01609, USA.

Biometrics
|September 14, 2000
PubMed
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This study introduces a Bayesian parametric model for time series of counts, effectively handling overdispersion and autocorrelation. The method uses historical data for prediction and a novel criterion for model assessment.

Area of Science:

  • Statistics
  • Biostatistics
  • Time Series Analysis

Background:

  • Correlated count data are common in repeated measures and time series.
  • Existing models may not adequately address both overdispersion and autocorrelation.

Purpose of the Study:

  • To develop a parametric model for time series of counts incorporating overdispersion and autocorrelation.
  • To propose a Bayesian approach with informative priors derived from historical data for enhanced prediction.
  • To introduce and evaluate a Bayesian criterion for assessing predictive performance.

Main Methods:

  • Constructed a likelihood-based parametric model for time series counts.
  • Developed informative prior distributions using historical data.
  • Derived the Bayesian predictive distribution and the predictive L measure.

Related Experiment Videos

  • Utilized yearly pollen count data for empirical evaluation.

    • Main Results:

    • The proposed model effectively incorporates overdispersion and autocorrelation.
    • Informative priors based on historical data improve predictions.
    • The predictive L measure provides a robust criterion for model comparison.

    Conclusions:

    • The Bayesian approach offers a flexible framework for analyzing correlated count time series.
    • The methodology is suitable for prediction tasks, particularly when historical data are available.
    • The predictive L measure facilitates rigorous comparison of different time series models.