Jackknife Variance Estimation under Imputation for Estimators Using Poststratification Information
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Poststratified estimators are commonly used in sample surveys to improve the efficiency of estimators and to ensure calibration to known poststrata counts. Similarly, generalized regression estimators are used to handle two or more poststratifiers with known marginal counts. In addition, weighting adjustment within weighting classes is used to handle unit nonresponse, and imputation within imputation classes is used to handle item nonresponse. For the full response case, asymptotic consistency of the jackknife variance estimator under stratified multistage sampling is established using mild regularity conditions on “residuals” similar to those of Scott and Wu for ratio and regression estimation under simple random sampling. A jackknife linearization variance estimator, obtained by linearizing the jackknife variance estimator, is also given. For unit nonresponse, the general case of poststrata cutting across weighting classes is considered, and a jackknife variance estimator and the corresponding jackknife linearization variance estimator are obtained. For item nonresponse, weighted mean imputation and weighted hot deck stochastic imputation within imputation classes are studied. Jackknife variance estimators, based on “adjusted” imputed values, are proposed, and the corresponding jackknife linearization variance estimators are obtained. Asymptotic consistency of the jackknife variance estimator is established for both the unit and item nonresponse cases under mild conditions on “residuals,” assuming uniform response within classes. Simulation results for the poststratified estimator under weighted mean imputation and weighted hot deck stochastic imputation are presented.
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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 | 0.005 |
| 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 | 0.001 |
| 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