A weighted method for the exclusive hypothesis test with application to typhoon data
Bibliographic record
Abstract
Motivated by the testing of genetic pleiotropy, we discuss a general class of hypothesis testing, the exclusive hypothesis test (EHT). A hypothesis test is an EHT if the null hypothesis can be divided into a set of exclusive sub‐hypotheses, and a main difficulty for testing an EHT is the calculation of the p ‐value. To address this problem, we propose a weighted procedure and develop two methods, one likelihood‐based and the other Bayesian information criterion (BIC)‐based, for determining the corresponding weights. Furthermore, we show that the BIC‐based method can control the asymptotic type I error. We conduct an extensive simulation study of these two proposed methods, which suggests that they work well in practice. In particular, the new procedure is shown both theoretically and numerically to exhibit better performance than the existing two‐stage decision rule for testing genetic pleiotropy. Our proposed methodology is then applied to a set of data concerning tropical storms.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".