Perturbation‐based null hypothesis tests with an application to Clayton models
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Bibliographic record
Abstract
Abstract Null hypothesis tests are popularly used when there is no appropriate alternative hypothesis available, especially in model assessment, where the assumed model is evaluated with no model being considered an alternative. Motivated by a test for Clayton models in multivariate survival analysis, we propose a perturbation‐based method for null hypothesis testing that makes use of the resampling approach in Jin et al. (Jin et al., Biometrika ; 2001; 88, 381–390) to estimate the variance–covariance matrix of an estimator to avoid intractable variance estimation. The proposed tests are straightforward and theoretically justified. We apply the proposed method to modify the tests in Shih (Shih, Biometrika ; 1998; 85, 189–200) for the assessment of Clayton models. The proposed tests present satisfactory performance in simulation studies. A colon cancer dataset further illustrates the proposed tests.
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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 | 0.003 |
| 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 it