An Alternative Hotelling T^2 Control Chart Based on Minimum Vector Variance (MVV)
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Bibliographic record
Abstract
The performance of traditional Hotelling T2 control chart using classical estimators in Phase I suffers from masking and swamping effect. To alleviate the problem, robust location and scale estimators are recommended. This paper proposed a robust Hotelling T2 control chart for individual observations based on minimum vector variance (MVV) estimators as an alternative to the traditional multivariate T2 control chart for Phase II data. MVV is a new robust estimator which possesses the good properties as in minimum covariance determinant (MCD) with better computational efficiency. Through simulation study, we evaluate the performance of the proposed chart in terms of probability of detection and false alarm rates and compared with the performance of the traditional charts and the chart issued from MCD estimators. The results showed that MVV control chart has competitive performance relative to MCD and traditional control charts even under certain location parameter shifts in Phase I data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 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