Simultaneous confidence intervals for mean differences of multiple zero-inflated gamma distributions with applications to precipitation
Why this work is in the frame
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Bibliographic record
Abstract
Changes in precipitation over different periods or regions are important because they have significant effects on many aspects of everyday life. Comparison of multiple forms of precipitation between several periods or regions may therefore be helpful as an aid to decision-making. Precipitation data have generally been assumed to follow a gamma distribution. However, since some dry days have zero precipitation, a zero-inflated gamma distribution is more appropriate for fitting the data. In this article, we consider three fiducial methods (one accurate method and two approximate) to construct simultaneous confidence intervals for mean differences of multiple zero-inflated gamma distributions. Our simulation studies show that the exact method gives more accurate results than the two approximate ones, and it is applicable to various situations. However, the two approximate methods are much faster than the exact one. Also, they provide satisfactory results when the shape parameters are large. Real data on three-year daily precipitations for Waterloo in Canada are used to illustrate the three methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it