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...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | Colombia |
| Institución: | Universidad Nacional de Colombia |
| Repositorio: | Repositorio UN |
| Idioma: | español |
| OAI Identifier: | oai:repositorio.unal.edu.co:unal/66547 |
| Acceso en línea: | https://repositorio.unal.edu.co/handle/unal/66547 http://bdigital.unal.edu.co/67575/ |
| Access Level: | acceso abierto |
| Palabra clave: | 51 Matemáticas / Mathematics 31 Colecciones de estadística general / Statistics Bias Mean Squared Error Multimodel Estimation Seroprevalence Error cuadrado medio Estimación multi-modelo Seroprevalencia Sesgo |
| Sumario: | 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. |
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