On stochastic dynamic modeling of incidence data

Emmanouil-Nektarios Kalligeris; Alex Karagrigoriou; Christina Parpoula

doi:10.1515/ijb-2021-0134

On stochastic dynamic modeling of incidence data

Int J Biostat. 2023 May 1. doi: 10.1515/ijb-2021-0134. Online ahead of print.

Authors

Emmanouil-Nektarios Kalligeris^{1

2}, Alex Karagrigoriou^{2

3}, Christina Parpoula⁴

Affiliations

¹ Laboratory of Mathematics Raphaël Salem, University of Rouen Normandy, Avenue de l'Université, BP. 12, 76801 Saint Étienne du Rouvray, Rouen, France.
² Lab of Statistics and Data Analysis, University of the Aegean, 83200 Karlovasi, Samos, Greece.
³ Graphic Era Deemed to be University, Dehradun, India.
⁴ Department of Psychology, Panteion University of Social and Political Sciences, 17671, Athens, Greece.

PMID: 37118931
DOI: 10.1515/ijb-2021-0134

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.

Keywords: elastic-net; epidemiological data; model selection; penalized likelihood.