A broad class of zero‐or‐one inflated regression models for rates and proportions
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 We introduce a family of distributions with bounded support for continuous rates or proportions when the data contain zeros or ones. On the basis of this class of distributions, we propose a novel class of regression models which is useful for modelling fractional data observed on [0, 1) or (0, 1]. The response variable of the new class of regression models has a mixed continuous‐discrete distribution with probability mass at zero or one, and the parameters of the mixture distribution are modelled through regression structures involving covariates and unknown parameters. An advantage of this class of regression models is the ability to deal with atypical observations. We consider a frequentist approach to performing inferences, and the traditional maximum likelihood method is employed to estimate the regression parameters. We also propose a residual analysis for the novel class of regression models to assess departures from model assumptions. Additionally, global and local influence methods are discussed. An empirical application that employs real data is considered to illustrate the usefulness of the new class of regression models in practice.
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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 | 0.000 | 0.003 |
| 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