Lifescience Global

ijsmr logo-pdf 1349088093

Conditional Two Level Mixture with Known Mixing Proportions: Applications to School and Student Level Overweight and Obesity Data from Birmingham, England
Pages 298-308
Shakir Hussain, Mehdi AL-Alak and Ghazi Shukur
Published: 05 August 2014

Abstract: 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.

Keywords: Parametric Expectation Maximization, Multilevel Mixture, Conditional Multilevel Mixture Known Mix, Overweight and Obesity Data.
Download Full Article
Submit to FacebookSubmit to Google PlusSubmit to TwitterSubmit to LinkedIn


♦  Worldwide readership
♦  High quality content
♦  Maximum visibility
♦  Efficient publishing
♦  Optional Open Access

Publish your Research