Accounting for Model Selection Uncertainty: Model Averaging of Prevalence and Force of Infection Using Fractional Polynomials

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 mo...

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Autores:: Castañeda, Javier
Aerts, Marc

Tipo de recurso:: Article of journal

Fecha de publicación:: 2015

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

Description
Summary:	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.

Accounting for Model Selection Uncertainty: Model Averaging of Prevalence and Force of Infection Using Fractional Polynomials

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