The perils of quasi‐likelihood information criteria
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Bibliographic record
Abstract
In this paper, we consider some potential pitfalls of the growing use of quasi‐likelihood‐based information criteria for longitudinal data to select a working correlation structure in a generalized estimating equation framework. In particular, we examine settings where the fully conditional mean does not equal the marginal mean as well as hypothesis testing following selection of the working correlation matrix. Our results suggest that the use of any information criterion for selection of the working correlation matrix is inappropriate when the conditional mean model assumption is violated. We also find that type I error differs from the nominal level in moderate sample sizes following selection of the form of the working correlation but improves as sample size is increased as the selection is then concentrated on a single correlation structure. Our results serve to underline the potential dangers that can arise when using information criteria to select correlation structure in routine data analysis. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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