Distribution-Free Bounds for Serial Correlation Coefficients in Heteroskedastic Symmetric Time Series
Bibliographic record
Abstract
Nous étudions le problème qui consiste à tester l'hypothèse que des observations X1, ..., Xn d'une série chronologique sont indépendantes avec des distributions non spécifiées (possiblement non identiques) symétriques autour d'une médiane connue. Nous proposons plusieurs bornes sur les distributions des coefficients d'autocorrélation : bornes exponen-tielles, bornes de type Eaton, bornes de Chebyshev et bornes de Berry-Esséen-Zolotarev. Les bornes sont exactes dans les échantillons finis, non paramétriques et faciles à calculer. Nous évaluons par simulation la performance des bornes et comparons celle-ci à celle de tests d'autocorrélation traditionnels. Les procédures proposées sont appliquées à des données de taux d'intérêt américaines ("commercial paper rate").
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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 | 0.003 | 0.023 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".