A generalized class of skew distributions and associated robust quantile regression models
Bibliographic record
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 Les auteurs présentent une classe généralisée de lois univariées asymétriques construites à partir du partitionnement de deux mélanges normalisés de lois normales (gaussiennes). Les lois proposées possèdent un paramètre d'asymétrie défini dans l'intervalle (0,1), permettant une application directe à la régression quantile paramétrique. L'utilisation de mélanges normalisés de lois normales permet une estimation efficace au moyen d'algorithmes de Monte-Carlo à chaî nes de Markov. Deux études de simulation portant sur la régression à erreur asymétrique et la régression quantile paramétrique révèlent des propriétés favorables pour l'estimation. Les auteurs illustrent les lois proposées et leur estimation pour ces deux types de modèles à l'aide d’études empiriques, une première portant sur l'analyse des rendements du marché des États-Unis, et une seconde à propos de données sur le poids de bébés à la naissance. La revue canadienne de statistique xx: 1–18; 2014 © 2014 Société statistique du Canada Additional supporting information may be found in the online version of this article at the publisher's web-site Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".