On stochastic dynamic modeling of incidence data-Reference-Cited by-同舟云学术

On stochastic dynamic modeling of incidence data

Published:2023-05-01 Issue:0 Volume:0 Page:
ISSN:2194-573X
Container-title:The International Journal of Biostatistics
language:en
Short-container-title:

Author:

Kalligeris Emmanouil-Nektarios¹²^ORCID,Karagrigoriou Alex²³,Parpoula Christina⁴

Affiliation:

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

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Link

https://www.degruyter.com/document/doi/10.1515/ijb-2021-0134/pdf

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