Linear Mixed-Effects Modeling by Parameter Cascading
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A linear mixed-effects model (LME) is a familiar example of a multilevel parameter structure involving nuisance and structural parameters, as well as parameters that essentially control the model’s complexity. Marginalization over nuisance parameters, such as the restricted maximization likelihood method, has been the usual estimation strategy, but it can involve onerous and complex algorithms to achieve the integrations involved. Parameter cascading is described as a multicriterion optimization algorithm that is relatively simple to program and leads to fast and stable computation. The method is applied to LME, where well-developed marginalization methods are already available. Our results suggest that parameter cascading is at least as good as, if not better than, the available methods. We also extend the LME model to multicurve data smoothing by introducing a basis partitioning scheme and defining roughness penalty terms for both functional fixed effect and random effects. The results are substantially better than those obtained by using the previous LME methods. A supplemental document is available online.
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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 | 0.045 |
| 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 | 0.001 |
| 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