Bibliographic record
Abstract
The authors show how Kendall's tau can be adapted to test against serial dependence in a univariate time series context.They provide formulas for the mean and variance of circular and non-circular versions of this statistic, and they prove its asymptotic normality.They present also a Monte Carlo study comparing the power and size of a test based on Kendall's tau to that of competing procedures based on alternative parametric and nonparametric measures of serial dependence.In particular, their simulations indicate that Kendall's tau outperforms Spearman's rho in detecting first-order autoregressive dependence, despite the fact that these two statistics are asymptotically equivalent. R ÉSUM ÉLes auteurs montrent comment le tau de Kendall peut être adapté pour tester la présence de dépendance sérielle dans une série chronologique univariée.Ils déterminent l'espérance et la variance de versions circulaire et non-circulaire de cette statistique et en démontrent la normalité asymptotique.Une étude de Monte-Carlo leur permet aussi de comparer le seuil et la puissance d'un test fondé sur cette statistique à celle de tests concurrents s'appuyant sur d'autres mesures paramétriques et non paramétriques de dépendance sérielle.Leurs simulations indiquent entre autres que le tau de Kendall détecte plus facilement la présence de dépendance autorégressive de premier ordre que le rho de Spearman, bien que ces deux statistiques soient asymptotiquement équivalentes.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".