Improved transformation-based quantile regression
Bibliographic record
Abstract
Modelling the quantiles of a random variable is facilitated by their equivariance to monotone trans-formations. In conditional modelling, transforming the response variable serves to approximate nonlinearrelationships by means of flexible and parsimonious models; these usually include standard transformationsas special cases. Transforming back to obtain predictions on the original scale or to calculate marginal non-linear effects becomes a trivial task. This approach is particularly useful when the support of the responsevariable is bounded. We propose novel transformation models for singly or doubly bounded responses,which improve upon the performance of conditional quantile estimators as compared to other competingtransformations, namely the Box–Cox and the Aranda-Ordaz transformations. The key is to provide flexibletransformations with range the whole of the real line. Estimation is carried out by means of a two-stageestimator, while confidence intervals are obtained by bootstrap. A simulation study and some illustrativedata analyses are presented.The Canadian Journal of Statistics43: 118–132; 2015©2015 StatisticalSociety of Canada
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".