Pitman closeness results for Type-I hybrid censored data from exponential distribution
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Bibliographic record
Abstract
Recent work on Pitman closeness has compared estimators under Type-II censored samples from an exponential distribution. More recently, a comparison was done for Type-I censored data wherein two estimators based on two different censoring times were compared in their estimation of θ under Pitman closeness. Extending such a comparison, one may consider alternate comparisons under Type-I hybrid censoring. In this paper, we consider the comparison of three associated estimators based on three hybrid censored samples and carry out the analogous Pitman closeness comparisons. Formulas are derived for the suitable Pitman closeness probabilities and numerical results are tabulated for a variety of settings. While most of the tabulated probabilities agree with an intuition that the estimator based on larger termination time should be Pitman closer, exceptions are found.
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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 | 0.006 |
| 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