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Forecasting obesity trends in England

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  • Terence C. Mills

Abstract

Summary. Forecasts of trends in obesity in England for 2010 are produced by treating the available data, which contain the proportions of the population, categorized by age and sex, falling into different body mass index ranges, as compositional data sets, so that the implicit simplex restrictions are automatically satisfied. Forecasts are calculated by using linear trend models for the log‐ratio transformations and are accompanied by prediction regions. The advantages of treating data on proportions compositionally are emphasized and compared with forecasts that have been obtained by ignoring this restriction.

Suggested Citation

  • Terence C. Mills, 2009. "Forecasting obesity trends in England," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 107-117, January.
    • Handle: RePEc:bla:jorssa:v:172:y:2009:i:1:p:107-117
    DOI: 10.1111/j.1467-985X.2008.00550.x
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    1. Wang, Huiwen & Liu, Qiang & Mok, Henry M.K. & Fu, Linghui & Tse, Wai Man, 2007. "A hyperspherical transformation forecasting model for compositional data," European Journal of Operational Research, Elsevier, vol. 179(2), pages 459-468, June.
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    Cited by:

    1. Peter Dawson & Paul Downward & Terence C. Mills, 2014. "Olympic news and attitudes towards the Olympics: a compositional time-series analysis of how sentiment is affected by events," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1307-1314, June.
    2. Kemp, Gordon C.R. & Santos Silva, J.M.C., 2012. "Regression towards the mode," Journal of Econometrics, Elsevier, vol. 170(1), pages 92-101.
    3. Duncan, Roberto & Toledo, Patricia, 2018. "Long-run overweight levels and convergence in body mass index," Economics & Human Biology, Elsevier, vol. 31(C), pages 26-39.
    4. Sarah Brown & William Greene & Mark Harris, 2020. "A novel approach to latent class modelling: identifying the various types of body mass index individuals," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 983-1004, June.
    5. Zheng, Tingguo & Chen, Rong, 2017. "Dirichlet ARMA models for compositional time series," Journal of Multivariate Analysis, Elsevier, vol. 158(C), pages 31-46.
    6. Duncan, Roberto & Toledo, Patricia, 2018. "Do overweight and obesity prevalence rates converge in Europe?," Research in Economics, Elsevier, vol. 72(4), pages 482-493.
    7. Terence Mills, 2010. "Forecasting compositional time series," Quality & Quantity: International Journal of Methodology, Springer, vol. 44(4), pages 673-690, June.

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