Bibliographic record
Abstract
Abstract Standard Gini covariance and Gini correlation play important roles in measuring the dependence between random variables with heavy tailed distributions. However the asymmetry of Gini covariance and correlation brings a substantial difficulty in interpretation. In this article we propose a symmetric Gini‐type covariance and a symmetric Gini correlation ( ) based on the joint rank function. The proposed correlation is more robust than the Pearson correlation but less robust than the Kendall's correlation. We establish the relationship between and the linear correlation for a class of random vectors in the family of elliptical distributions, which allows us to estimate based on estimation of . The asymptotic normality of the resulting estimators of is studied through two approaches: one based on influence function and the other based on U‐statistics and the delta method. We compare asymptotic efficiencies of the symmetric Gini, regular Gini, Pearson and Kendall's linear correlation estimators under various distributions. In addition to reasonably balancing between robustness and efficiency, the proposed measure shows superior finite sample performance, which makes it attractive in applications. The Canadian Journal of Statistics 44: 323–342; 2016 © 2016 Statistical Society of Canada
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".