Clustering Profiles in Generalized Linear Mixed Models Settings Using Bayesian Nonparametric Statistics
Bibliographic record
Abstract
Generalized linear mixed models are used to model clustered and longitudinal data in which the distribution of the response variable is a member of the exponential family.This thesis introduces a novel method for simultaneous clustering of such data and estimation of parameters of the underlying generalized linear mixed models.Clustering has been extensively studied for both cross-sectional and longitudinal data.In longitudinal data, one has to take into account the association between observations taken on the same individual.This has found applications in epidemiology, genetics, biology, market research, economics, and many other areas.Generalized linear mixed models consist of two sets of parameters: fixed effects parameters that associate covariates to the response at the population level, and random effects parameters that associate covariates to the response at the individual level.We introduce a method that identifies homogeneous groups in the data based on similarities among random effects parameters that are obtained when homogeneous groups are modeled using generalized linear mixed models.We achieve this by placing a Dirichlet Process prior on random effects parameters, which induces clustering of random effects and subsequently the clustering of profiles.As a result, our method simultaneously groups profiles into clusters and estimates model
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".