Estimating Function Jackknife Variance Estimators Under Stratified Multistage Sampling
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Bibliographic record
Abstract
Abstract Generalized regression (GREG) uses auxiliary variables with known population totals to improve efficiency of estimators and to ensure consistency with the known totals. Variance estimation for the GREG estimator of a total under stratified multistage sampling is considered. Customary resampling methods (jackknife, balanced repeated replication and bootstrap) for estimating the variance of a GREG estimator require the inversion of a P × P matrix for each resample, where P is the number of auxiliary variables with known population totals. This could lead to illconditioned matrices for some of the resamples. We apply the estimating function (EF) resampling method of Hu and Kalbfleisch [Hu, F., Kalbfleisch, J. D. (2000). The estimating function bootstrap (with discussion). Can. J. Statist. 28:449–499] to obtain variance estimators, using jackknife resampling. This method avoids repeated inverses. We extend the results to cover parameters defined as solutions of census estimating equations. The proposed method can be implemented from micro data files containing the GREG weights and the associated EF jackknife weights.
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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 | 0.009 | 0.006 |
| 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