Predictive modeling of obesity prevalence for the U.S. population
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| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20190014601 | ||
| 003 | MAP | ||
| 005 | 20190524085525.0 | ||
| 008 | 190521e20190301esp|||p |0|||b|spa d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | 1 | $0MAPA20190006378$aDaawin, Palma | |
| 245 | 1 | 0 | $aPredictive modeling of obesity prevalence for the U.S. population$cPalma Daawin, Seonjin Kim, Tatjana Miljkovic |
| 520 | $aModeling 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. | ||
| 650 | 4 | $0MAPA20080592059$aModelos predictivos | |
| 650 | 4 | $0MAPA20080548421$aObesidad | |
| 650 | 4 | $0MAPA20080555306$aMortalidad | |
| 651 | 1 | $0MAPA20080638337$aEstados Unidos | |
| 700 | 1 | $0MAPA20190006385$aKim, Seonjin | |
| 700 | 1 | $0MAPA20190006392$aMiljkovic, Tatjana | |
| 773 | 0 | $wMAP20077000239$tNorth American actuarial journal$dSchaumburg : Society of Actuaries, 1997-$x1092-0277$g01/03/2019 Tomo 23 Número 1 - 2019 , p. 64-81 |