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Pairwise likelihood estimation of latent autoregressive count models.

Xanthi Pedeli1, Cristiano Varin2

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Summary

This study introduces a weighted pairwise likelihood approach for analyzing infectious disease time series data. This method offers a computationally efficient alternative to traditional simulation-based approximations for latent autoregressive models.

Keywords:
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Area of Science:

  • Epidemiology
  • Biostatistics
  • Computational Statistics

Background:

  • Latent autoregressive models are crucial for infectious disease time series analysis.
  • Evaluating the likelihood function of these models is computationally challenging.
  • Current simulation-based methods are intensive and difficult to validate.

Purpose of the Study:

  • To propose a computationally efficient and robust alternative to simulation-based methods for latent autoregressive models.
  • To explore the computational and methodological aspects of a weighted pairwise likelihood approach.
  • To apply the proposed method to real-world infectious disease data.

Main Methods:

  • Weighted pairwise likelihood estimation.
  • Estimation of robust standard errors.
  • Investigation of numerical integration techniques.
  • Application to monthly invasive meningococcal disease data from Greece and Italy.

Main Results:

  • The weighted pairwise likelihood approach provides a feasible alternative for likelihood evaluation.
  • The method allows for robust standard error estimation.
  • Numerical integration plays a key role in the computational efficiency and accuracy.

Conclusions:

  • The weighted pairwise likelihood approach is a viable and computationally advantageous method for latent autoregressive models in infectious disease analysis.
  • This approach offers improved assessment of model approximations.
  • The method demonstrated effectiveness on invasive meningococcal disease data.