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Conditional two level mixture with known mixing proportions: applications to school and student level overweight and obesity data from Birmingham, England
University of Birmingham.
Central Organization for Statistics, Baghdad.
Linnaeus University, School of Business and Economics, Department of Economics and Statistics. (Statistik)ORCID iD: 0000-0002-3416-5896
2014 (English)In: International Journal of Statistics in Medical Research, ISSN 1929-6029, Vol. 3, no 3, 298-308 p.Article in journal (Refereed) Published
Abstract [en]

Two Level (TL) models allow the total variation in the outcome to be decomposed as level one and level two or ‘individual and group’ variance components. Two Level Mixture (TLM) models can be used to explore unobserved heterogeneity that represents different qualitative relationships in the outcome.

In this paper, we extend the standard TL model by introducing constraints to guide the TLM algorithm towards a more appropriate data partitioning. Our constraints-based methods combine the mixing proportions estimated by parametric Expectation Maximization (EM) of the outcome and the random component from the TL model. This forms new two level mixing conditional (TLMc) approach by means of prior information. The new framework advantages are: 1. avoiding trial and error tactic used by TLM for choosing the best BIC (Bayesian Information Criterion), 2. permitting meaningful parameter estimates for distinct classes in the coefficient space and finally 3. allowing smaller residual variances. We show the benefit of our method using overweight and obesity from Body Mass Index (BMI) for students in year 6. We apply these methods on hierarchical BMI data to estimate student multiple deprivation and school Club effects.

Place, publisher, year, edition, pages
2014. Vol. 3, no 3, 298-308 p.
Keyword [en]
Parametric expectation maximization, multilevel mixture, Conditional multilevel mixture known mix, Overweight and obesity data
National Category
Probability Theory and Statistics Health Sciences
Research subject
Statistics/Econometrics
Identifiers
URN: urn:nbn:se:lnu:diva-37680DOI: 10.6000/1929-6029.2014.03.03.9OAI: oai:DiVA.org:lnu-37680DiVA: diva2:756028
Available from: 2014-10-15 Created: 2014-10-15 Last updated: 2016-12-14Bibliographically approved

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Shukur, Ghazi
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Department of Economics and Statistics
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf