Comparison of the Power of Lognormality Tests with Different Right-Tail Alternative Distributions
Bibliographic record
Abstract
In flood frequency analysis (FFA), the adequate choice of distribution to fit data is a major problem. The three-parameter lognormal (LN3) distribution has an intermediate tail behavior between the distributions of the Class C (regularly varying distributions) and those of the Class D (subexponential distributions). HYFRAN software performs a complete frequency analysis for approximately twenty distributions often used in hydrology including the LN3 and distributions of Classes C and D. A decision support system (DSS) was added to the HYFRAN software to become the HYFRAN-PLUS software. It allows distinguishing between the distributions of Classes C and D. The objective of the present study is to discriminate between the LN3 distribution and that of Class C of regularly varying distributions (heavier tail) and D of subexponential distributions (lighter tail) and then to improve the current version of the DSS. The power of several normality tests is evaluated for log-transformed variates using a Monte Carlo approach for different alternative hypothesis. The Jarque-Bera test has been found the most powerful on transformed data. Results show a strong dependence between the values of the parameters and the power of the test as well as the quantile estimation errors. Results lead to the development of a LN3 goodness-of-fit procedure, based on the coefficient of variation, the coefficient of skewness and the Jarque-Bera normality test. This procedure will be added to the Decision Support System of the HYFRAN-PLUS software.
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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 | 0.069 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".