A Rescaling Bootstrap Approach For Imputed Survey Data
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Imputation is usually used to deal with item nonresponse in surveys. Treating the imputed values as true observations may obviously lead to serious underestimation of the variance of point estimators. In this article, we propose a new bootstrap method under the rescaling bootstrap approach for estimating the variance of an imputed estimator obtained after applying deterministic regression or random hot-deck imputation. A novel technique is used to rescale the original data set through solving certain systems of linear equations. The proposed procedure can handle unequal response probabilities and large sampling fractions. Some simulation studies are conducted to show the great performance of the proposed method in terms of relative bias, relative efficiency, and coverage probability, for both population mean and median.
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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 | 0.039 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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