EM‐based likelihood inference for one‐shot device test data under log‐normal lifetimes and the optimal design of a CSALT plan
Bibliographic record
Abstract
Abstract One‐shot devices result in an extreme case of interval censoring, wherein one can only know whether the failure time is either before or after the test time. The study of one‐shot device testing has been developed considerably recently, both in terms of estimation and optimal design under different lifetime distributions. However, one‐shot device testing analysis under lognormal lifetime distribution has not been studied yet. While the hazard function for exponential distribution is always a constant, and that of Weibull and gamma are either increasing or decreasing, the lognormal distribution has increasing ‐ decreasing behavior of hazard which is encountered often in practice as units usually experience early failure and then stabilize over time in terms of performance. In this paper, we develop the EM algorithm for the likelihood estimation based on one‐shot device test data under lognormal distribution and also the design of optimal CSALTs (constant stress accelerated life tests) under this set up with budget constraints. A simulation study is carried out to assess the performance of the methods of inference developed here and some real‐life data are analyzed for illustrative purpose.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".