Bayesian Quantile Regression Based on the Generalized Gamma Distribution
Bibliographic record
Abstract
Quantile regression seeks to extend classical least square regression by modeling quantiles of the conditional distribution of the response given the observed covariates. The attributes of quantile regression and its potential to handle different types of distributions, makes it possible to get rid of relying on normality assumptions and to solve problems in a more logical structure. It therefore provides crucial means to recognize effects that would not be noticed in classical least square regression. This study investigates Bayesian estimation of the 3-parameter generalized gamma distribution in the context of quantile regression, by allowing dependence of the model parameters on a covariate. The quantiles of the generalized gamma distribution are functions of the parameters, and in turn functions of the covariate. Our Bayesian estimation approach is compared to the maximum likelihood approach. Our work is validated via simulations to study the performance of the estimation methods. To demonstrate the use of the estimation methods, a data of corrosion is analyzed. Based on the Akaike Information Criterion (AIC) and/or Deviance Information Criterion (DIC), we infer that the model with interaction effects better fits the data.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".