Generalized pseudo empirical likelihood inferences for complex surveys
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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