Optimal and Robust Designs of Step-stress Accelerated Life Testing Experiments for Proportional Hazards Models
Bibliographic record
Abstract
Accelerated life testing (ALT) is widely used to obtain reliability information about a product within a \nlimited time frame. The Cox s proportional hazards (PH) model is often utilized for reliability prediction. \nMy master thesis research focuses on designing accelerated life testing experiments for reliability estimation. \nWe consider multiple step-stress ALT plans with censoring. The optimal stress levels and times of changing \nthe stress levels are investigated. We discuss the optimal designs under three optimality criteria. They are \nD-, A- and Q-optimal designs. We note that the classical designs are optimal only if the model assumed is \ncorrect. Due to the nature of prediction made from ALT experimental data, attained under the stress levels \nhigher than the normal condition, extrapolation is encountered. In such case, the assumed model cannot be \ntested. Therefore, for possible imprecision in the assumed PH model, the method of construction for robust \ndesigns is also explored.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".