Estimating the mean squared prediction error of the observed best predictor associated with small area counts: A computationally oriented approach
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We consider estimation of the mean squared prediction error (MSPE) for observed best prediction (OBP) in small area estimation with count data. The OBP method has been previously developed in this context by Chen et al. ( Journal of Survey Statistics and Methodology , 3, 136–161, 2015). However, estimation of the MSPE remains a challenging problem due to potential model misspecification that is considered in this setting. The latter authors proposed a bootstrap method for estimating the MSPE, whose theoretical justification is not clear. We propose to use a Prasad–Rao‐type linearization method to estimate the MSPE. Unlike the traditional linearization approaches, our method is computationally oriented and easier to implement in the same regard. Theoretical properties and empirical performance of the proposed method are studied. A real‐data application is considered.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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