Empirical likelihood inference for multiple censored samples
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 present a semiparametric approach to inference on the underlying distributions of multiple right‐ and/or left‐censored samples with fixed censoring points and focus on effective estimation of population quantiles and distribution functions. We pool information across multiple censored samples through a semiparametric density ratio model and propose an empirical likelihood approach to inference. This approach achieves high efficiency without making restrictive model assumptions. The resultant estimator is asymptotically normal, and the resulting distribution function estimator and quantile estimator are more efficient than estimators obtained from the classic nonparametric methods, such as the empirical distribution and sample quantile. In addition, the proposed approach permits consistent estimation of distribution functions and quantiles on a larger domain than would otherwise be possible using the classic methods. Simulation studies suggest that the proposed method is robust against misspecification of the density ratio function and against outliers. Our approach is further illustrated with an application to the analysis of real lumber strength data. The Canadian Journal of Statistics 46: 212–232; 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 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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