Robust Rao-type tests for step-stress accelerated lifetests with interval-censored data and Weibull lifetime distributions
Bibliographic record
Abstract
Many engineering products are highly reliable in the present highly competitive market, often exhibiting long mean lifetimes to failure. This makes experimental testing both time-intensive and challenging. Accelerated life-tests are commonly used to induce early failures by subjecting products to higher-than-normal stress conditions, enabling enough failures to be observed for accurate statistical analysis. Additionally, censored data is a common challenge in reliability studies. Specifically, interval-censored data arises when continuous monitoring of devices is impractical or infeasible due to technical constraints or budget limitations. Statistical inference in such situations is often based on the likelihood function of the model. However, likelihood-based methods can be highly sensitive to outliers, which may result in biased or unreliable estimates. To address this issue, minimum density power divergence techniques can be used as a robust alternative. These methods extend traditional likelihood-based approach and have demonstrated appealing performance in reliability inference. In this paper, we develop robust restricted estimators based on the density power divergence for step-stress accelerated life-tests under Weibull distributions with interval-censored data and use these restricted estimators to generalize the Rao Score test for testing composite null hypotheses, including testing the significance of stress factors contributing degradation of the devices. We present the theoretical asymptotic properties of the estimators and also associated test statistics, along with numerical analyses that support the robustness of the proposed estimators and tests of hypotheses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".