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An Algorithm for Prior Elicitation in Dynamic Bayesian Models for Proportions with the Logit Link Function

Author

Listed:
  • James D. Santos

    (Federal University of Amazonas)

  • José M. J. Costa

    (Federal University of Amazonas)

Abstract

The elicitation of hyperparameters at time t in dynamic Bayesian models for proportions is performed by solving a nonlinear system. This paper presents an algorithm to solve this system when the logit function is used. If the initial conditions are satisfied, it is guaranteed that the algorithm converges to the solution. The performance of some dynamic models was compared using the standard method and the new method presented here, using simulated data and the monthly series of deaths from tuberculosis sequelae in the state of São Paulo, Brazil.

Suggested Citation

  • James D. Santos & José M. J. Costa, 2019. "An Algorithm for Prior Elicitation in Dynamic Bayesian Models for Proportions with the Logit Link Function," Methodology and Computing in Applied Probability, Springer, vol. 21(1), pages 169-183, March.
    • Handle: RePEc:spr:metcap:v:21:y:2019:i:1:d:10.1007_s11009-018-9642-3
    DOI: 10.1007/s11009-018-9642-3
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Kostas Triantafyllopoulos, 2009. "Inference of Dynamic Generalized Linear Models: On‐Line Computation and Appraisal," International Statistical Review, International Statistical Institute, vol. 77(3), pages 430-450, December.
    3. da-Silva, C.Q. & Migon, H.S. & Correia, L.T., 2011. "Dynamic Bayesian beta models," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2074-2089, June.
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