Dutch case studies of the estimation of extreme quantiles and associated uncertainty by bootstrap simulations
Bibliographic record
Abstract
Abstract The article presents several practical applications of the peaks‐over‐threshold (POT) method to the estimation of extreme quantiles of environmental variables, such as sea level, river discharge, precipitation, wave height and earthquake magnitude using actual data collected in the Netherlands. The quantile estimation by the POT method is conceptually simple, since it involves fitting a Pareto distribution to peaks of a time series exceeding a high threshold. However, practical applications of the POT method are confounded by the selection of a suitable threshold, since quantile estimates tend to exhibit large and erratic variation with threshold. The article illustrates this threshold sensitivity of quantile estimates in a variety of data sets. Specifically, the article compares the performance of L‐moment and de Haan methods for modelling peak data by the Pareto distribution. To evaluate the quantile bias and variance as functions of threshold, a semi‐parametric bootstrap algorithm is utilized. The article deliberately emphasizes the use of conceptually simple and practical methods to promote engineering applications of statistical theory of extremes. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".