Estimation of location and scale functionals in nonparametric regression under copula dependent censoring
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 Let be a random vector, where Y denotes the variable of interest possibly subject to random right censoring, and X is a covariate. The variable Y is a (possible monotone transformation of a) survival time. The censoring time C and the survival time Y are allowed to be dependent, and the dependence is described via a known copula (this also includes the independent case). Under this setting we propose estimators of certain location and scale functionals of Y given X . We derive their asymptotic properties, uniformly over the support of X . In particular we derive an asymptotic representation and the uniform convergence rates for these estimators and their derivatives. We also prove asymptotic results for an estimator of the conditional distribution (the so‐called conditional copula‐graphic estimator), which generalizes previous results obtained by Braekers & Veraverbeke (2005). We also illustrate the results via simulations and the analysis of data on bone marrow transplantation. The Canadian Journal of Statistics 43: 306–335; 2015 © 2015 Statistical Society 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.
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 | 0.005 |
| 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