Exponentiality Test Procedures for Large Samples of Rainfall Event Characteristics
Bibliographic record
Abstract
The main purpose of this paper is to examine and recommend procedures that can be used to statistically test the exponentiality of large amounts of sample data for rainfall event volume, duration, and interevent time. Based on literature review and initial analysis, the Poisson and chi-square goodness-of-fit tests are selected first. Some misconceptions about parameter estimators and degrees of freedom associated with the use of the chi-square goodness-of-fit tests are then clarified. Using rainfall data from seven stations in the north-central region of the United States, the choice of the event volume threshold and the minimum interevent time for separating continuous rainfall data into individual events are examined in detail. Findings from this study suggest that the Poisson test can be used for testing the exponentiality of interevent times and for examining the statistical independence of consecutive rainfall events. The use of the minimum chi-square estimator combined with the chi-square goodness-of-fit test is recommended for rainfall event volume and duration. An equation that can be used to determine the appropriate number of bins for grouping sample data when conducting the chi-square goodness-of-fit tests is also proposed.
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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 | 0.106 | 0.428 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".