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On stochastic dynamic modeling of incidence data

Author

Listed:
  • Kalligeris Emmanouil-Nektarios

    (Laboratory of Mathematics Raphaël Salem, University of Rouen Normandy, Avenue de l’Université, BP. 12, 76801 Saint Étienne du Rouvray, Rouen, France)

  • Karagrigoriou Alex

    (Lab of Statistics and Data Analysis, University of the Aegean, 83200 Karlovasi, Samos, Greece)

  • Parpoula Christina

    (Department of Psychology, Panteion University of Social and Political Sciences, 17671, Athens, Greece)

Abstract

In this paper, a Markov Regime Switching Model of Conditional Mean with covariates, is proposed and investigated for the analysis of incidence rate data. The components of the model are selected by both penalized likelihood techniques in conjunction with the Expectation Maximization algorithm, with the goal of achieving a high level of robustness regarding the modeling of dynamic behaviors of epidemiological data. In addition to statistical inference, Changepoint Detection Analysis is performed for the selection of the number of regimes, which reduces the complexity associated with Likelihood Ratio Tests. Within this framework, a three-phase procedure for modeling incidence data is proposed and tested via real and simulated data.

Suggested Citation

  • Kalligeris Emmanouil-Nektarios & Karagrigoriou Alex & Parpoula Christina, 2024. "On stochastic dynamic modeling of incidence data," The International Journal of Biostatistics, De Gruyter, vol. 20(1), pages 201-215.
    • Handle: RePEc:bpj:ijbist:v:20:y:2024:i:1:p:201-215:n:1001
    DOI: 10.1515/ijb-2021-0134
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