Proceedings of the Survey Methods Section BENCHMARKING HIERARCHICAL BAYES SMALL AREA ESTIMATORS WITH APPLICATION IN CENSUS UNDERCOVERAGE ESTIMATION
Bibliographic record
Abstract
Linear mixed effects models such as the Fay-Herriot model (1979) and non-linear mixed effects models such as the unmatched area level models proposed by You and Rao (2002) have been used in small area estimation to obtain efficient model-based small area estimators. It is often desirable to benchmark the model-based estimates so that they add up to the direct survey estimates for large areas to protect against possible model mis-specification and possible overshrinkage. In this paper, hierarchical Bayes (HB) unmatched area level models are considered. Posterior means and posterior variances of parameters of interest are first obtained using the Gibbs sampling method. Then we benchmark the HB estimators (posterior means) to obtain the benchmarked HB (BHB) estimators. Posterior mean squared error (PMSE) is then used as a measure of uncertainty for the BHB estimators. The PMSE can be represented as the sum of the usual posterior variance and a bias correction term. We evaluate the HB and the BHB estimators in the application of Canadian census undercoverage estimation. The sum of the provincial BHB census undercount estimates is equal to the direct survey estimate of the census undercount for the whole nation.
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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 | 0.092 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".