Order Restricted Testing of Random Effects in Generalized Linear Mixed Models
Bibliographic record
Abstract
Generalized linear mixed models (GLMM) have been used in many areas of research to analyze longitudinal and clustered data with non-normal responses.In addition to the fixed effects parameters found in the generalized linear model (GLM), variance components associated with unobservable random effects are estimated in the GLMM.Moreover, it is well understood that order restricted inference methods that properly incorporate additional information by way of a restricted parameter space are more efficient than procedures that ignore this information.In this thesis, a distance statistic based on the Wald statistic is suggested for order restricted tests on the random components in the mixed model.The null distributions of the distance and the likelihood ratio test statistics are shown to be asymptotically equivalent and that of a chi-bar-square.An analysis conducted on data extracted from the 2011 National Youth Tobacco Survey will serve as an illustration of the proposed testing procedure.
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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 | 0.098 | 0.429 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".