Assessment of an L-Kurtosis-Based Criterionfor Quantile Estimation
Bibliographic record
Abstract
The estimation of extreme quantiles corresponding to small probabilities of exceedance is commonly required in the risk analysis of flood protection structures. The usefulness of L-moments has been well recognized in the statistical analysis of data, because they can be estimated with less uncertainty than that associated with traditional moment estimates. The objective of the paper is to assess the effectiveness of L-kurtosis in the method of L-moments for distribution fitting and quantile estimation from small samples. For this purpose, the performance of the proposed L-kurtosis-based criterion is compared against a set of benchmark measures of goodness of fit, namely, divergence, integrated-square error, chi square, and probability-plot correlation. The divergence is a comprehensive measure of probabilistic distance used in the modern information theory for signal analysis and pattern recognition. Simulation results indicate that the L-kurtosis criterion can provide quantile estimates that are in good agreement with benchmark estimates obtained from other robust criteria. The remarkable simplicity of the computation makes the L-kurtosis criterion an attractive tool for distribution selection.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".