Techniques for the construction of robust regression designs
Bibliographic record
Abstract
Abstract The authors review and extend the literature on robust regression designs. Even for straight line regression, there are cases in which the optimally robust designs—in a minimax mean squared error sense, with the maximum evaluated as the “true” model varies over a neighbourhood of that fitted by the experimenter—have not yet been constructed. They fill this gap in the literature, and in so doing introduce a method of construction that is conceptually and mathematically simpler than the sole competing method. The technique used injects additional insight into the structure of the solutions. In the cases that the optimality criteria employed result in designs that are not invariant under changes in the design space, their methods also allow for an investigation of the resulting changes in the designs. The Canadian Journal of Statistics 41: 679–695; 2013 © 2013 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 | 0.067 | 0.194 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".