Testing uniformity for the case of a planar unknown support
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Bibliographic record
Abstract
Abstract A new test is proposed for the hypothesis of uniformity on bi‐dimensional supports. The procedure is an adaptation of the “distance to boundary test” (DB test) proposed in Berrendero, Cuevas, & Vázquez‐Grande (2006). This new version of the DB test, called DBU test, allows us (as a novel, interesting feature) to deal with the case where the support S of the underlying distribution is unknown. This means that S is not specified in the null hypothesis so that, in fact, we test the null hypothesis that the underlying distribution is uniform on some support S belonging to a given class ${\cal C}$ . We pay special attention to the case that ${\cal C}$ is either the class of compact convex supports or the (broader) class of compact λ‐convex supports (also called r ‐convex or α‐convex in the literature). The basic idea is to apply the DB test in a sort of plug‐in version, where the support S is approximated by using methods of set estimation. The DBU method is analysed from both the theoretical and practical point of view, via some asymptotic results and a simulation study, respectively. The Canadian Journal of Statistics 40: 378–395; 2012 © 2012 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 | 0.015 |
| 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