Graphical lassos for meta‐elliptical distributions
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
Abstract Gaussian graphical lasso is a tool for estimating sparse graphs using a Gaussian log‐likelihood with an penalty on the inverse covariance matrix. This paper proposes a generalization to meta‐elliptical distributions. Conditional uncorrelatedness is characterized in meta‐elliptical families. The proposed meta‐elliptical and re‐weighted Kendall graphical lassos are computed from pseudo‐observations which are functions of ranks of observations. They are invariant to strictly increasing transformations of the variables and do not assume the existence of moments. Simulations of receiver operating characteristic curves show noticeable improvements (in comparison with graphical lassos designed for meta‐Gaussian distributions) for distributions which are not meta‐Gaussian. These improvements are realized without ill effects when the distribution is meta‐Gaussian. Deterministic and random contaminations of data are used to verify the robustness of the re‐weighted Kendall graphical lasso. The Canadian Journal of Statistics 42: 185–203; 2014 © 2014 Statistical Society of Canada
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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 | 0.018 |
| 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