Bibliographic record
Abstract
A preliminary test approach using shrinkage technique is proposed for the estimation of a Poisson parameter. In this article, an a priori value of the parameter is assumed to be available in the form of realistic guessed value based on the experimenter's knowledge and experience to increase the precision of estimators by using a preliminary test. It is to be noted that this approach differs from the Bayesian approach since we do not assume a prior distribution for the parameter. Three possible estimators, namely the unrestricted maximum likelihood estimator (UMLE), the shrinkage restricted maximum likelihood estimator (SRMLE) and the shrinkage preliminary test maximum likelihood estimator (SPTMLE), are presented. Asymptotic mean squared errors of the estimators are derived and compared analytically and numerically. The relative dominance picture of the estimators is presented. It is shown that the range in the parameter space which SPTMLE dominates the UMLE is wider than that of usual preliminary test maximum likelihood estimator (PTMLE). Further, the SPTMLE provides more meaningful size for the preliminary test than the usual PTMLE. A Monte Carlo study provided to compare the performance of the estimators for small sample sizes.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".