Clarifying Some Aspects of Variance Estimation in Two-Phase Sampling
Bibliographic record
Abstract
We consider the problem of variance estimation in two-phase sampling designs. The usual variance estimators suffer from two drawbacks: their computation requires specialized software designed for two-phase sampling, and they depend on the second-phase joint inclusion probabilities, which may be difficult to obtain. We consider a simplified variance estimator and study its properties with respect to several two-phase designs. The extension to calibration estimators is considered. We establish interesting links between the proposed simplified variance estimator and resampling variance estimators studied in Kott and Stukel (1997) and Kim, Navarro, and Fuller (2006). In particular, we shed new light on a long-standing issue that has been raised in Kott and Stukel (1997). These authors showed that the jackknife method leads to a consistent variance estimator of the reweighted estimator but to an inconsistent variance estimator of the double expansion estimator. We give a simple explanation why this is so.
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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 | 0.073 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".