Bivariate exponentiated generalized inverted exponential distribution with applications on dependent competing risks data
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Bibliographic record
Abstract
<p>This paper introduces a novel bivariate distribution derived from the univariate exponentiated generalized inverted exponential (EGIE) distribution, which we term the bivariate exponentiated generalized inverted exponential (BEGIE) distribution. The newly proposed distribution belongs to the Marshall-Olkin class. Several statistical attributes of the BEGIE distribution are explored. The utility of this distribution is examined through applications on both bivariate data and dependent competing risks data. Estimation processes for the model's parameters, using maximum likelihood and Bayesian methods, are outlined for scenarios involving both bivariate and dependent competing risks data. Due to the absence of closed-form solutions for these estimators, numerical optimization techniques are employed. Furthermore, the proposed distribution is illustrated and evaluated through the analysis of three real datasets: two involving bivariate data, and the other involving dependent competing risks data.</p>
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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