Abstract

CASTANEDA, JAVIER  and  AERTS, MARC. Accounting for Model Selection Uncertainty: Model Averaging of Prevalence and Force of Infection Using Fractional Polynomials. Rev.Colomb.Estad. [online]. 2015, vol.38, n.1, pp.163-179. ISSN 0120-1751.  https://doi.org/10.15446/rce.v38n1.48808.

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 cross-sectional 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.

Keywords : Bias; Mean Squared Error; Multimodel Estimation; Seroprevalence.

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