Modeling Evolving Dependence between Bivariate Extremes through Multivariate Distortion Functions
Bibliographic record
Abstract
Probability distortion has been a means of pricing in insurance and finance for a long time. It is often utilized to transform the loss probability distribution to another distribution that assigns more weight to the outstanding potential losses. Parametric models for multivariate distributions can be proposed based on the extension of distortion transformations to the multivariate framework, which allows for generating new families of copulas from an existing one. These parametric representations are used in order to relate the distribution of bivariate climate extreme realizations to the distribution of projected extremes in the long term. The focus of this thesis is on modeling the bivariate distribution of temperature and precipitation annual maxima in Montreal by Extreme Value Theory, and propose a distortion of this model to explain the future projections based on three emissions scenarios representing different atmospheric concentrations of greenhouse gases (RCP 2.6, RCP 4.5 and RCP 8.5). Lastly, Akaike information criterion (AIC) and Bayesian information criterion (BIC) are employed to compare the performance of different distortions.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".