Robust Control Charts for Monitoring Process Variability in Phase I Multivariate Individual Observations
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Bibliographic record
Abstract
Multivariate control charts are widely used in various industries to monitor the shifts in process mean and process variability. In Phase I monitoring, control limits are computed using the historical data, and control charts based on classical estimators (sample mean and the sample covariance) are highly sensitive to the outliers in the data. We propose robust control charts with high breakdown robust estimators based on the re‐weighted minimum covariance determinant and the re‐weighted minimum volume ellipsoid to monitor the process variability of multivariate individual observations in Phase I data under multivariate exponentially weighted mean square error and multivariate exponentially weighted moving variance schemes. The control limits are computed empirically, and the performance of the proposed charts is assessed with Monte Carlo simulations by considering different data scenarios. The proposed robust control charts are shown to perform better than charts based on classical estimators. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.026 |
| 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 | 0.001 |
| 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