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Modelling Body Mass Index Distribution using Maximum Entropy Density

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Open access
Authors
Singh, Ranjodh
Chan, F.
Harris, Mark
Date
2015
Type
Conference Paper

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Singh, R. and Chan, F. and Harris, M.N. 2015. Modelling Body Mass Index Distribution using Maximum Entropy Density, in Proceedings of the 21st International Congress on Modelling and Simulation, Nov 28 - Dec 4 2015. Gold Coast, Queensland: Modelling and Simulation Society of Australia and New Zealand (MODSIM).
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21st International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand
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Faculty
Faculty of Business and Law
School
School of Economics, Finance and Property
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Abstract

The objective of this paper is to model the distribution of Body Mass Index (BMI) for a given set of covariates. BMI is one of the leading indicators of health and has been studied by health professionals for many years. As such, there have been various approaches to model the distribution of BMI. Furthermore, there are numerous studies which investigate the association between an individual’s physical and socio-economic attributes (covariates) to their BMI levels. This paper proposes the use of Maximum Entropy Density (MED) to model the distribution of BMI using information from covariates. The paper shows how covariates can be incorporated into the MED framework. This framework is then applied to an Australian data set. The results show how different covariates affect different moments of the estimated BMI distribution.

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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0