Model Mis-Specification Analyses of Weibull and Gamma Models Based on One-Shot Device Test Data
Bibliographic record
Abstract
Model mis-specification is of great importance in reliability assessment. Different choices of probability models for fitting data may result in substantially different inferential results on some lifetime characteristics of interest. Gamma and Weibull models have been used extensively for modeling lifetime data. Hence, accelerated life models have been developed recently for one-shot device test data under both these models for making inference on mean lifetime as well as the reliability at use level. However, model mis-specification analyses between these two models have not been studied in this context. Here, we examine the effect of model mis-specification between gamma and Weibull models on the likelihood estimation and the inference on the mean lifetime and the reliability at some mission times based on one-shot device test data. Moreover, a distance-based test statistic and the Akaike information criterion as specification tests are studied for the purpose of model validation. A simulation study is carried out to evaluate the bias and coverage probabilities of confidence intervals under model mis-specification. The obtained results reveal that the effect of model mis-specification is negligible only when the sample size is small and when the accelerated and use levels are close, and that the use of specification test is quite important for an accurate reliability assessment.
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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 | 0.018 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".