Predictive modeling of obesity prevalence for the U.S. population

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<rdf:Description>
<dc:creator>Daawin, Palma</dc:creator>
<dc:creator>Kim, Seonjin</dc:creator>
<dc:creator>Miljkovic, Tatjana</dc:creator>
<dc:date>2019-03-01</dc:date>
<dc:description xml:lang="es">Sumario: Modeling obesity prevalence is an important part of the evaluation of mortality risk. A large volume of literature exists in the area of modeling mortality rates, but very few models have been developed for modeling obesity prevalence. In this study we propose a new stochastic approach for modeling obesity prevalence that accounts for both period and cohort effects as well as the curvilinear effects of age. Our model has good predictive power as we utilize multivariate ARIMA models for forecasting future obesity rates. The proposed methodology is illustrated on the U.S. population, aged 2390, during the period 19882012. Forecasts are validated on actual data for the period 20132015 and it is suggested that the proposed model performs better than existing models.</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/168015.do</dc:identifier>
<dc:language>spa</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Modelos predictivos</dc:subject>
<dc:subject xml:lang="es">Obesidad</dc:subject>
<dc:subject xml:lang="es">Mortalidad</dc:subject>
<dc:subject xml:lang="es">Estados Unidos</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Predictive modeling of obesity prevalence for the U.S. population</dc:title>
<dc:relation xml:lang="es">En: North American actuarial journal. - Schaumburg : Society of Actuaries, 1997- = ISSN 1092-0277. - 01/03/2019 Tomo 23 Número 1 - 2019 , p. 64-81</dc:relation>
<dc:coverage xml:lang="es">Estados Unidos</dc:coverage>
</rdf:Description>
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