Bayesian weighted composite linear expectile regression
Bibliographic record
Abstract
Abstract Compared with ordinary least squares (LS) estimation, expectile regression (ER) is more robust to heavy‐tailed errors or outliers in the response variable. However, ER only considers single expectile information and does not determine whether the selected expectile is appropriate. To overcome this problem, we propose a weighted composite expectile regression (WCER) method, which can effectively resist the occurrence of heavy‐tailed errors or outliers. This article studies weighted composite sparse linear ER under high‐dimensional conditions from a Bayesian perspective and extends the linear model to the Tobit model. The benefit of the Bayesian hierarchical framework is that the weights of each component in the composite model can be treated as open parameters, which can be automatically estimated using Markov Chain Monte Carlo (MCMC) sampling. Finally, simulation and empirical analysis illustrate that this method is superior to the single expectile method.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".