Robust inference for an interval-monitored step-stress experiment with competing risks for failure with an application to capacitor data
Why this work is in the frame
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Bibliographic record
Abstract
Accelerated life-tests (ALTs) are applied for inferring lifetime characteristics of highly reliable products. In some cases, due to cost or product nature constraints, continuous monitoring of devices is infeasible and so the units are inspected at particular inspection time points, resulting in interval-censored responses. Furthermore, when a test unit fails, there is often more than one competing risk. In this paper, we assume that all competing risks are independent and follow an exponential distribution depending on the stress level. Under this setup, we present a family of robust estimators based on the density power divergence (DPD), including the classical maximum likelihood estimator as a particular case. We then derive asymptotic and robustness properties of the minimum DPD estimators (MDPDEs). Based on these MDPDEs, estimates of some lifetime characteristics of the product as well as estimates of some cause-specific lifetime characteristics are developed. Direct, transformed and bootstrap confidence intervals are proposed, and their performance is empirically compared through Monte Carlo simulations. The methods of inference discussed in this work are finally illustrated with a real-data example regarding electronic devices. • Reliability analysis under censorship needs ad-hoc statistical methods. • Different failure (competing risks) causes may lead to system failure. • Accelerated life-tests infer the lifetime of highly reliable devices. • Robust divergence-based estimators resist data contamination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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 it