Doubly robust imputation procedures for finite population means in the presence of a large number of zeros
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Bibliographic record
Abstract
Abstract Single imputation is often used in surveys to compensate for item nonresponse. In some cases, the variable requiring imputation contains a large amount of zeros. This is especially frequent in business surveys that collect economic variables. Motivated by a mixture regression model, we propose three imputation procedures and study their properties in terms of bias and variance. We show that these procedures are doubly robust, leading to consistent estimators of the finite population mean if either the imputation model or the nonresponse model is well specified. For the proposed procedures, we consider a jackknife variance estimator, which is consistent for the true variance, provided the overall sampling fraction is negligible. Finally, the results of a simulation study comparing the performance of point and variance estimators in terms of relative bias and mean square error are presented. The Canadian Journal of Statistics 42: 650–669; 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 | 0.001 | 0.007 |
| 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