Estimating Extremal Dependence in Univariate and Multivariate Time\n Series via the Extremogram
Bibliographic record
Abstract
Davis and Mikosch [7] introduced the extremogram as a flexible quantitative\ntool for measuring various types of extremal dependence in a stationary time\nseries. There we showed some standard statistical properties of the sample\nextremogram. A major difficulty was the construction of credible confidence\nbands for the extremogram. In this paper, we employ the stationary bootstrap to\novercome this problem. Moreover, we introduce the cross extremogram as a\nmeasure of extremal serial dependence between two or more time series. We also\nstudy the extremogram for return times between extremal events. The use of the\nstationary bootstrap for the extremogram and the resulting interpretations are\nillustrated in several univariate and multivariate financial time series\nexamples.\n
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".