A new family of copula-based concordance orderings of random pairs: Properties and nonparametric tests
Bibliographic record
Abstract
The formal assessment of the stochastic dominance of a random pair with respect to another one is a question of interest in the economic analysis of populations. For example, a manager may wonder if the components of a portfolio are more associated than that of another competing portfolio, in which case the former is generally considered more at risk. In this paper, a new family of copula-based concordance orderings in the spirit of increasing convex and concave orderings of random pairs is introduced as a natural extension of the well-known concordance ordering. In addition, a complete statistical methodology to test the stochastic dominance of a random pair with respect to another one according to the new concordance orderings is developed. The proposed tests are nonparametric, consistent against all alternatives, and valid under serially dependent data satisfying the α-mixing assumption. The sampling properties of the tests are investigated with the help of Monte–Carlo simulations and their usefulness is illustrated on real multivariate data.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".