Survey estimation of domain means that respect natural orderings
Bibliographic record
Abstract
Abstract Many variables in surveys follow natural orderings that should be respected in estimates of domain means. For instance the U.S. National Compensation Survey estimates mean wages for many job categories, and these mean wages are expected to be non‐decreasing according to job level. In this type of situation isotonic regression can be applied to give constrained estimators satisfying the monotonicity. We combine domain estimation and the pooled adjacent violators algorithm to construct new design‐weighted constrained estimators. The resulting estimator is the classical design‐based domain estimator but after adaptive pooling of neighbouring domains, so that it is both readily implemented in large‐scale surveys and easy to explain to data users. Under mild conditions on the sampling design and the population we obtain the asymptotic properties of the estimator. Simulation results also demonstrate improved point estimators and confidence intervals for domain means using linearization‐ and replication‐based variance estimation compared to survey estimators that do not incorporate the constraints. The Canadian Journal of Statistics 44: 431–444; 2016 © 2016 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 | 0.025 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".