A Monte Carlo Simulation Comparison of Some Nonparametric Survival Functions for Incomplete Data
Bibliographic record
Abstract
This article compares a new piecewise exponential estimator (NPEE) of a survival function for censored data with other three famous estimators Kaplan-Meier estimator (KME), Nelson estimator (NE), and an empirical Bayes type estimator (EBE) found in the literature. The NPEE, which is continuous on [0;1), retains the spirit of the KME and provides an exponential tail with a hazard rate determined by a novel nonparametric consideration while the other three estimators have limited usage because of their various shortcomings. In our simulation study, we employed absolute bias and relative efficiency as measures of quality of the models. We chose three levels of censoring and two sample sizes and did comparisons at various quantiles. It is found that the NPEE, which is asymptotically equivalent to the KME, is shown to be better than the other three estimators for finite samples.
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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 | 0.021 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".