Comparison Between Three Statistical Methods for the Extreme Value Analysis of Waves and the Projection of Return Periods
Bibliographic record
Abstract
The primary aim of this investigation is to conduct a comparative analysis on the anticipated intervals at which significant wave height (Hs) will occur.The spectral partition technique was used to separate time series.Next, they utilized three established methods for extreme value analysis: (1) Initial Distribution: This method assumes a specific probability distribution for the data and estimates the return period for extreme Hs values based on that distribution.(2) Peak Over Threshold (POT): This approach identifies exceedances of a chosen threshold (a significant wave height) and analyzes those extreme events to estimate return periods.(3) Annual Maximums: Here, the highest Hs value for each year is extracted, and the return period is estimated based on this series of annual maxima.By analyzing extremes in both the combined data and each individual series, the researchers discovered that one series likely contributes more significantly to extreme Hs values within the overall dataset.This suggests that the separate series might represent different wave regimes with varying influences on extreme events.The study emphasizes the benefits of applying extreme value analysis (EVA) to independent wave data series.Furthermore, the peak over threshold statistical method exhibits heightened statistical robustness and improved reliability in predicting return periods using wave data.
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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 | 0.035 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".