Bibliographic record
Abstract
In this thesis, we explore different estimation methods for longitudinal data with binary responses and drop-outs.We also study the effect of incorrectly specifying the dependence structure or the drop-out mechanism in longitudinal data.Although highly efficient, the traditional maximum likelihood (ML) method becomes complex when the number of responses increases, requiring intensive computation.Alternative methods such as generalized estimating equations (GEE) and weighted GEE had been proposed in the literature to overcome the limitation of the ML method.However, both estimators are known to be biased under non-ignorable drop-out mechanisms.The bivariate maximum pseudo likelihood is a pseudo likelihood method that takes into account the correlation between the current and baseline responses.Originally developed for non-monotone missing data, it was modified to be adapted for monotone drop-outs.We conduct a simulation study to assess the sensitivity of each method to model misspecifications in which a non-ignorable drop-out mechanism is our primary interest.
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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 | 0.077 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".