Analysis of censored data under heteroscedastic transformation regression models with unknown transformation function
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Bibliographic record
Abstract
Abstract Consider a censored heteroscedastic transformation regression model where both the transformation function and the error distribution function are completely unknown. A method is developed to estimate the transformation function, the regression parameter vector, and the single index parameter vector of the variance function by establishing an expression for the transformation function and two estimating equations for both the parameter vectors. It is shown that the estimator of the transformation function converges weakly to a mean zero Gaussian process, and the parametric estimators are asymptotically normal. All the estimators converge to their true values in probability at a rate proportional to . Simulation studies are conducted to evaluate the finite sample behaviour of the proposed estimators, and a real data analysis is used to illustrate the proposed estimating method. The Canadian Journal of Statistics 46: 233–245; 2018 © 2017 Statistical Society of Canada
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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 | 0.001 | 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 | 0.001 |
| 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