Objective Priors for Model Selection in One-Way Random Effects Models.” Submitted to The Canadian journal of Statistics
Bibliographic record
Abstract
It is broadly accepted that the Bayes factor is a key tool in model selection. Nevertheless, it is an important, difficult and still open question which priors should be used to develop objective (or default) Bayes factors. We consider this problem in the context of the one-way random effects model. Arguments based on concepts like orthogonality, matching predictive, and invariance are used to justify a specific form of the priors, in which the (proper) prior for the new parameter (using Jeffreys ’ terminology) has to be determined. Two different proposals for this proper prior have been derived: the intrinsic priors and the divergence based priors, a recently proposed methodology. It will be seen that the divergence based priors produce consistent Bayes factors. The methods are illustrated on examples and compared with other proposals. Finally, the divergence based priors and the associated Bayes factor are derived for the unbalanced case.
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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 | 0.046 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".