Marshall-Olkin Extended Generalized Exponential Distribution: Properties, Inference and Application to Traffic Data
Bibliographic record
Abstract
This paper aims to develop a three-parameter distribution called the Marshall–Olkin Extended Generalized Exponential (MOEGE ) distribution, which can be used in analyzing both reliability and survival data. Some statistical properties of the new distribution have been studied, which include, moments, incomplete moments, Renyl entropy, stochastic ordering, order statistics, and the moment generating function. The MOEGE distribution has submodels such as the Marshall–Olkin Extended Exponential (MOEE) , the Generalized Exponential (GE), and the Exponential (E) distribution. The maximum likelihood estimation technique is used to obtain the parameters estimate of the MOEGE distribution, also, we constructed a 95% asymptotic confidence interval for the parameters. The performances of the estimators have been studied using Monte Carlo simulation, and finally, to demonstrate the applicability of the MOEGE distribution, a traffic data set has been used.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".