The new family of Fisher copulas to model upper tail dependence and radial asymmetry: Properties and application to high‐dimensional rainfall data
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Bibliographic record
Abstract
Joint precipitation data measured at a large number of stations typically show tail asymmetry and significant upper tail dependence. Unfortunately, many multivariate dependence models that are commonly used in large dimensions such as the normal and the Student copulas are radially symmetric, whereas the recently introduced chi‐square copula is asymmetric, but its tail dependence coefficients are null. In order to circumvent the limitations of the available models, the new family of Fisher copulas is introduced; it is shown that these dependence models are tail asymmetric and allow for upper tail dependence, among other characteristics. Two semiparametric strategies for parameter estimation in this class of copulas are proposed, and their efficiency in small and moderate sample sizes is investigated with the help of simulations. The usefulness of the parametric Fisher copula family is then illustrated on the modeling of the precipitation data observed at 105 stations within or close to the Aare river catchment in Switzerland.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it