Statistical inferences for the extended inverse Weibull distribution under progressive type-II censored sample with applications
Why this work is in the frame
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Bibliographic record
Abstract
This paper is concerned with making statistical inference on extended inverse Weibull (EXIW) distribution under type-II censored sample distribution. This distribution posses a lot of marvels statistical properties, such as linear representation, and many other properties among them the incomplete moment which has been provided in addition to the stress strength reliability function, and moments. To conduct a thorough study of this distribution, we estimated its parameters using both classical and non-classical techniques on both progressive censored and complete data. In order to find the best and efficient method of estimation we made a simulation study and using its results we mentioned which method is the best. We used modified algorithms to find the fitting of the data to the EXIW distribution. In the end, but certainly not least, we developed an application by making use of the EXIW distribution in order to evaluate its superiority in comparison to its competitors.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it