Varying-Coefficient Marginal Models and Applications in Longitudinal Data Analysis
Why this work is in the frame
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Bibliographic record
Abstract
We consider a class of nonparametric marginal models in which the regres-sion coefficients are assumed to be time-varying smooth functions. Such models are appealing in longitudinal data analysis to characterize the time-dependent effects of covariates on the expected value of the response vari-able. A local quasi-likelihood method is employed to estimate the coefficient functions, based on the nonparametric technique of local polynomial kernel regression. We establish the asymptotic distribution theory for the estima-tors considered. We conduct Monte Carlo simulation studies to compare two types of kernel-based GEE methods with global and local variance struc-tures, respectively. We illustrate the proposed models via three real-world data sets from a clinical trial of multiple sclerosis, a quality of life study in chemotherapeutic treatments on breast cancer, and a genomic fine-scale mapping association study on chromosomal region 5q31 for Crohn’s disease. AMS (2000) subject classification. Primary 62G08, 62J12, 62P10.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it