Generalized Augmentation for Control of the k-Familywise Error Rate
Bibliographic record
Abstract
When performing many hypothesis tests at once a correction for multiplicity is needed to both keep under control the number of false discoveries and be able to detect the true departures from the null hypotheses. A recently introduced method which has been proved to be useful in genomics, neuroimaging and other fields consists in probabilistically controlling that the number of falsely rejected hypotheses does not exceed a pre-specified (low) . We introduce a new multiple testing procedure which is based on the idea of generalized augmentation: at first a number of hypotheses is rejected without any correction, then this number is adjusted by adding or removing rejections. The procedure is shown to keep under control the probability of or more false rejections. We show a small simulation study which suggests that the new procedure is very powerful, especially when the number of tests at stake is large. We conclude with an illustration on a benchmark data set on classification of colon cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.151 | 0.352 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".