ROBUST EXTREME RETURN LEVEL WITH POWER NORMALIZATION FOR EXTREME EVENTS: APPLICATION OF REAL HYDROLOGY DATA
Bibliographic record
Abstract
In statistical studies of rare and catastrophic phenomena the distribution of generalized extreme values under linear normalization is always chosen as the appropriate model. It used to estimate the probabilities of events that have not yet been observed. Recently, the extreme value theory (EVT) received a lot of attention both theoretically and practically using just the classical linear model (L-Model) or linear normalization of the maximum to estimate return level. So, in this paper we propose a new multiplicative model based on the distribution of generalized extreme values under non-linear normalization, whose purpose is to raise the strong and weak points between these two models. Our main goal is to use our multiplicative model (P-model) to calculate the return level, as well as the associated confidence interval. The diagnostic fit, test and statistical inference to compare the two models (linear and non-linear) are studied. Finally, a data analysis and discussion are applied at first on real hydrological data for Morocco and South of Australia, then on water levels of lake Erié in Canada. The results show that our multiplicative (non-linear) model is more adaptive because it takes into account the variation of the return period.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".