Bayesian Robustness to Outliers in Linear Regression and Ratio\n Estimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Whole robustness is a nice property to have for statistical models. It\nimplies that the impact of outliers gradually vanishes as they approach plus or\nminus infinity. So far, the Bayesian literature provides results that ensure\nwhole robustness for the location-scale model. In this paper, we make two\ncontributions. First, we generalise the results to attain whole robustness in\nsimple linear regression through the origin, which is a necessary step towards\nresults for general linear regression models. We allow the variance of the\nerror term to depend on the explanatory variable. This flexibility leads to the\nsecond contribution: we provide a simple Bayesian approach to robustly estimate\nfinite population means and ratios. The strategy to attain whole robustness is\nsimple since it lies in replacing the traditional normal assumption on the\nerror term by a super heavy-tailed distribution assumption. As a result, users\ncan estimate the parameters as usual, using the posterior distribution.\n
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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