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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22797

Title: Accounting for Model Selection Uncertainty: Model Averaging of Prevalence and Force of Infection Using Fractional Polynomials
Other Titles: Un método para la inclusión de la incertidumbre en la selección del modelo: promedio de modelos para la prevalencia y la fuerza de infección usando polinomios fraccionarios
Authors: Castañeda, Javier
Aerts, Marc
Issue Date: 2015
Citation: Revista Colombiana de Estadística, 38(1), p. 163-179
Abstract: In most applications in statistics the true model underlying data generation mechanisms is unknown and researchers are confronted with the critical issue of model selection uncertainty. Often this uncertainty is ignored and the model with the best goodness-of-fit is assumed as the data generating model, leading to over-confident inferences. In this paper we present a methodology to account for model selection uncertainty in the estimation of age-dependent prevalence and force of infection, using model averaging of fractional polynomials. We illustrate the method on a seroprevalence crosssectional sample of hepatitis A, taken in 1993 in Belgium. In a simulation study we show that model averaged prevalence and force of infection using fractional polynomials have desirable features such as smaller mean squared error and more robust estimates as compared with the general practice of estimation based only on one selected “best” model.
URI: http://hdl.handle.net/1942/22797
DOI: 10.15446/rce.v38n1.48808
ISSN: 0120-1751
Category: A1
Type: Journal Contribution
Validation: vabb, 2018
Appears in Collections: Research publications

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