In large observational studies, data are often highly multivariate with many discrete and continuous variables measured on each observational unit. One often derives subpopulations to facilitate analysis. Traditional approaches suggest modeling such subpopulations with a compilation of interaction effects. However, when many interaction effects define each subpopulation, it becomes easier to model membership in a subpopulation rather than numerous interactions. In many cases, subjects are not complete members of a subpopulation but rather partial members of multiple subpopulations. Grade of Membership scores preserve the integrity of this partial membership. By generalizing an analytic chemistry concept related to chromatography-mass spectrometry, we obtain a method that can identify latent subpopulations and corresponding Grade of Membership scores for each observational unit.
College and Department
Physical and Mathematical Sciences; Statistics
BYU ScholarsArchive Citation
Eliason, Ryan Lee, "Application of Convex Methods to Identification of Fuzzy Subpopulations" (2010). All Theses and Dissertations. 2242.
Grade of Membership scores, archetype, maximum entropy, fuzzy partitioning