Novel Logistic Extreme Value Distribution: Properties, Applications, and Parameter Estimation Using Classical and Machine Learning Methods
Bibliographic record
Abstract
The life testing and analysis are important in many disciplines, such as medicine, engineering, and finance.Indeed, probability distributions are one of the critical components of accurate modelling as they govern the effectiveness and robustness of statistical evaluations.This study introduces the Novel Logistic Extreme Value Distribution (NLEVD), a flexible three-parameter probability distribution that generalizes the Logistic Extreme Value Distribution.The mathematical properties of NLEVD, including its probability density function, cumulative distribution function, moment-generating function, entropy, and order statistics, are derived.Parameter estimation was conducted via Maximum Likelihood Estimation (MLE) and Support Vector Machine (SVM) techniques, which demonstrated improved accuracy.The proposed model is validated using two real-world datasets, on which it outperforms established lifetime distributions, such as the New Extension Exponential, Gamma-Lindley, Zeghdoudi, X Lindley, and X gamma distributions.The results highlight NLEVD's superior ability to model diverse failure rate behaviors, making it a powerful tool for survival analysis, reliability engineering, and applied statistics.This study provides a robust alternative for modelling lifetime data, offering greater flexibility and precision in statistical modelling.
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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 | 0.021 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".