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dc.contributor.authorBrown, S.
dc.contributor.authorGreene, William
dc.contributor.authorHarris, Mark
dc.date.accessioned2020-11-26T07:05:35Z
dc.date.available2020-11-26T07:05:35Z
dc.date.issued2020
dc.identifier.citationBrown, S. and Greene, W. and Harris, M. 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: Statistics in Society. 183 (3): pp. 983-1004.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/81791
dc.identifier.doi10.1111/rssa.12552
dc.description.abstract

© 2020 Royal Statistical Society

Given the increasing prevalence of adult obesity, furthering understanding of the determinants of measures such as the body mass index (BMI) remains high on the policy agenda. We contribute to existing literature on modelling the BMI by proposing an extension to latent class modelling, which serves to unveil a more detailed picture of the determinants of BMI. Interest here lies in latent class analysis with a regression model and predictor variables explaining class membership, a regression model and predictor variables explaining the outcome variable within BMI classes and instances where the BMI classes are naturally ordered and labelled by expected values within class. A simple and generic way of parameterizing both the class probabilities and the statistical representation of behaviours within each class is proposed, that simultaneously preserves the ranking according to class-specific expected values and yields a parsimonious representation of the class probabilities. Based on a wide range of metrics, the newly proposed approach is found to dominate the prevailing approach and, moreover, results are often quite different across the two.

dc.languageEnglish
dc.publisherWILEY
dc.subjectSocial Sciences
dc.subjectScience & Technology
dc.subjectPhysical Sciences
dc.subjectSocial Sciences, Mathematical Methods
dc.subjectStatistics & Probability
dc.subjectMathematical Methods In Social Sciences
dc.subjectMathematics
dc.subjectBody mass index
dc.subjectExpected values
dc.subjectLatent class models
dc.subjectObesity
dc.subjectOrdered probability models
dc.subjectHEALTH-CARE
dc.subjectOBESITY
dc.subjectSELECTION
dc.subjectSTANDARD
dc.subjectAKAIKE
dc.subjectTRENDS
dc.subjectIMPACT
dc.subjectADULT
dc.subjectTESTS
dc.subjectORDER
dc.titleA novel approach to latent class modelling: identifying the various types of body mass index individuals
dc.typeJournal Article
dcterms.source.volume183
dcterms.source.number3
dcterms.source.startPage983
dcterms.source.endPage1004
dcterms.source.issn0964-1998
dcterms.source.titleJournal of the Royal Statistical Society. Series A: Statistics in Society
dc.date.updated2020-11-26T07:05:35Z
curtin.note

This is the peer reviewed version of the following article: Brown, S., Greene, W. and Harris, M. (2020), A novel approach to latent class modelling: identifying the various types of body mass index individuals. J. R. Stat. Soc. A, 183: 983-1004. which has been published in final form at [https:// doi.org/10.1111/rssa.12552. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.

curtin.departmentSchool of Economics, Finance and Property
curtin.accessStatusOpen access
curtin.facultyFaculty of Business and Law
curtin.contributor.orcidHarris, Mark [0000-0002-1804-4357]
dcterms.source.eissn1467-985X
curtin.contributor.scopusauthoridHarris, Mark [35561581200] [55310794400]
curtin.contributor.scopusauthoridGreene, William [7202789151]


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