Bayesian Pseudo-Empirical-Likelihood Intervals for Complex Surveys
Bibliographic record
Abstract
Summary Bayesian methods for inference on finite population means and other parameters by using sample survey data face hurdles in all three phases of the inferential procedure: the formulation of a likelihood function, the choice of a prior distribution and the validity of posterior inferences under the design-based frequentist framework. In the case of independent and identically distributed observations, the profile empirical likelihood function of the mean and a non-informative prior on the mean can be used as the basis for inference on the mean and the resulting Bayesian empirical likelihood intervals are also asymptotically valid under the frequentist set-up. For complex survey data, we show that a pseudo-empirical-likelihood approach can be used to construct Bayesian pseudo-empirical-likelihood intervals that are asymptotically valid under the design-based set-up. The approach proposed compares favourably with a full Bayesian analysis under simple random sampling without replacement. It is also valid under general single-stage unequal probability sampling designs, unlike a full Bayesian analysis. Moreover, the approach is very flexible in using auxiliary population information and can accommodate two scenarios which are practically important: incorporation of known auxiliary population information for the construction of intervals by using the basic design weights; calculation of intervals by using calibration weights based on known auxiliary population means or totals.
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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 | 0.030 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".