Generalized pseudo empirical likelihood inferences for complex surveys
Bibliographic record
Abstract
Abstract We consider generalized pseudo empirical likelihood inferences for complex surveys. The method is based on a weighted version of the Kullback–Leibler (KL) distance for calibration estimation (Deville & Särndal, 1992) and includes the pseudo empirical likelihood estimator (Chen & Sitter, 1999; Wu & Rao, 2006) and the calibrated likelihood estimator (Tan, 2013) as special cases. We show that a suitably formulated empirical likelihood ratio‐type statistic follows asymptotically a scaled chi‐square distribution, which extends the main result in Wu & Rao (2006) and makes the likelihood ratio‐type confidence intervals available for calibration estimation using arbitrary choices of the weighting factor in the weighted KL distance. We further show that the scaling factor for the scaled chi‐square distribution can be circumvented either through a particular choice of the weighting factor for the KL distance or using a bootstrap method. The proposed bootstrap procedure is justified for single‐stage sampling designs with negligible sampling fractions. Finite sample performances of confidence intervals constructed using our proposed methods are investigated and compared with existing ones through two simulation studies. The Canadian Journal of Statistics 43: 1–17; 2015 © 2015 Statistical Society of Canada
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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 | 0.023 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".