Estimating Function Jackknife Variance Estimators Under Stratified Multistage Sampling
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".