A generalized class of skew distributions and associated robust quantile regression models
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
Abstract This article proposes a generalized class of univariate skew distributions that are constructed through partitioning two scaled mixture of normal (Gaussian) distributions. The proposed distributions have a skewness parameter defined in the interval (0,1), allowing direct application to parametric quantile regression. Employing scale mixture of normals facilitates efficient estimation via Markov chain Monte Carlo methods. Two simulation studies, one on estimation with skew error regression models, the other on parametric quantile regression models reveal favourable estimation properties. Two corresponding empirical studies, one analysing U.S. market returns, the other on infant birthweight data further illustrate the proposed distributions and their estimation. The Canadian Journal of Statistics 42: 579–596; 2014 © 2014 Statistical Society of Canada
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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) | 0.000 | 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