On variance estimation under auxiliary value imputation in sample surveys
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
We study the problem of variance estimation for a domain total when auxiliary value imputation, sometimes called cold-deck or substitution imputation, has been used to fill in missing data. We consider two approaches to inference which lead to different variance estimators. In the first approach, the validity of an impu- tation model is required. Our proposed variance estimator is nevertheless robust to misspecification of the second moment of the model. Under this approach, we show the somewhat counter-intuitive result that the total variance of the imputed estima- tor can be smaller than the sampling variance of the complete-data estimator. We also show that the na¨ ive variance estimator (i.e. the variance estimator obtained by treating the imputed values as observed values) is a consistent estimator of the total variance when the sampling fraction is negligible. In the second approach, the validity of an imputation model is not required but response probabilities need to be estimated. Our mean squared error estimator is obtained using robust estimates of response probabilities and is thus only weakly dependent on modeling assump- tions. We also show that both approaches lead to asymptotically equivalent total mean squared errors provided that the imputation model underlying the imputed estimator is correctly specified and the sampling fraction is negligible. Finally, we propose a hybrid variance estimator that can be viewed as a compromise between the two approaches. A simulation study illustrates the robustness of our proposed variance (mean squared error) estimators.
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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 | 0.003 | 0.008 |
| 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