A novel Compound-Pareto model with applications and reliability peaks above a random threshold value at risk analysis
Bibliographic record
Abstract
This paper aims to model the bimodal and right-skewed aircraft windshield data using a novel compounded-Pareto distribution. The method of maximum likelihood is employed to estimate the unknown model parameters, and the performance of the estimators under finite samples is evaluated through a comprehensive simulation study. The practical applicability of the proposed model is demonstrated using two real-world reliability datasets. Reliability analysis based on Peaks Over a Random Threshold Value at Risk (PORT-VAR) is crucial for aircraft windshield manufacturers, as it provides a rigorous assessment of extreme failure events and service times-key factors in ensuring product safety and longevity. By identifying the frequency and severity of failures exceeding specific VAR thresholds, this analysis enables companies to understand the upper bounds of their products' performance under stress, optimize designs for enhanced durability, and develop proactive maintenance strategies. In this paper, we present a comprehensive reliability PORT-VAR analysis to support these objectives and highlight the relevance of the proposed model in extreme value risk modeling and real-world reliability scenarios.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".