Robust Control Charts for Monitoring Process Mean of Phase-I Multivariate Individual Observations
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Bibliographic record
Abstract
Hoteling's <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msup><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math> control charts are widely used in industries to monitor multivariate processes. The classical estimators, sample mean, and the sample covariance used in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msup><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math> control charts are highly sensitive to the outliers in the data. In Phase-I monitoring, control limits are arrived at using historical data after identifying and removing the multivariate outliers. We propose Hoteling's <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:msup><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math> control charts with high-breakdown robust estimators based on the reweighted minimum covariance determinant (RMCD) and the reweighted minimum volume ellipsoid (RMVE) to monitor multivariate observations in Phase-I data. We assessed the performance of these robust control charts based on a large number of Monte Carlo simulations by considering different data scenarios and found that the proposed control charts have better performance compared to existing methods.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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