Bootstrap methods for imputed data from regression, ratio and hot‐deck imputation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Item non‐response in sample surveys is usually addressed by imputation. A bootstrap method that treats the imputed values as if they were observed generally leads to variance estimates that are too small. Shao & Sitter (1996) introduced a bootstrap method in this context, which leads to consistent variance estimators when the sampling fraction is small. In the context of stratified simple random sampling, we introduce the independent bootstrap, which is valid even when the sampling fraction is large. It consists of modifying a bootstrap method for sample surveys, of independently generating the response status of each unit, and of imputing the non‐respondents in the bootstrap sample. We pay special attention to the bootstrap survey weights approach of Rao, Wu, & Yue (1992). The Canadian Journal of Statistics 42: 142–167; 2014 © 2014 Statistical Society of Canada
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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 | 0.002 | 0.014 |
| 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