Iterative Regression Algorithm for Parameter Estimation for Nondestructive One-Shot Devices Under Cyclic Accelerated Life Test With Adaptive Proportion of Failure Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One-shot devices, such as automotive airbags, fire extinguishers and ammunitions, pose significant challenges in their reliability analysis due to their inherently unobservable lifespans. Nondestructive one-shot devices, in particular, offer additional information when they have not failed prior to inspection, yielding interval-censored failure time data. This article addresses the limitations of traditional testing designs for such devices by introducing an adaptive proportion of failure approach within the context of cyclic accelerated life tests, a variant of accelerated life tests characterized by continuously varying stress levels in the operating environment. Using the Norris–Landzberg model for thermal cycling-induced stresses, we propose here an iterative regression algorithm for statistical inference under this adaptive design. Our algorithm provides estimators that possess consistency and asymptotic normality, demonstrating robustness against initial value sensitivity, a common issue with traditional numerical methods used for maximum likelihood estimation. A simulation study and an illustrative example are presented to exemplify the merits of the proposed approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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