A balanced two-sided CUSUM chart for monitoring time between events
Why this work is in the frame
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Bibliographic record
Abstract
The time-between-event (TBE) charts are used to monitor the failure rate λ by examining the time interval T between events. This article proposes a two-sided CUSUM chart (the balanced two-sided CUSUM chart ) for detecting both increasing and decreasing shifts in λ . The performance studies show that the balanced CUSUM chart can significantly improve the overall performance across the entire process shift range. On average, this chart is more effective than the conventional two-sided CUSUM chart by nearly 20%. Meanwhile, the proposed chart is relatively easy to be designed and does not increase the difficulty of implementation. An asymmetrical loss function is also proposed as the objective function for the design of the balanced CUSUM chart. It takes into consideration of the asymmetry of the probability distribution of T and the different impacts of the increasing and decreasing shifts in λ .
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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 | 0.003 | 0.022 |
| 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 | 0.000 | 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 it