Monitoring multivariate coefficient of variation for high‐dimensional processes
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Multivariate coefficient of variation (MCV) charts are effective tools for monitoring process relative variability. They are developed on the assumption that the process subgroup size available for monitoring the MCV parameter is larger than the number of process characteristics. In such a case, the unbiased estimates of the in‐control mean vector and covariance matrix are used to calculate the chart monitoring statistic. Here, we study the performance of MCV control charts when only a small subgroup size is available for estimating the in‐control mean vector and covariance matrix. We examine the use of a shrinkage estimate of the covariance matrix and propose two one‐sided upward and downward least absolute shrinkage and selection operator (LASSO)‐based MCV charts for detecting upward and downward shifts in the process MCV parameter, respectively. Our simulation study shows that the LASSO‐based MCV charts outperform the classical two one‐sided MCV charts when small subgroup sizes are available for monitoring. The improved performance of the proposed LASSO‐based MCV charts in monitoring shifts in the MCV parameter is demonstrated via an illustrative case study of carbon fiber tube application, where changes are detected earlier than the classical two one‐sided MCV charts.
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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 | 0.016 |
| 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 | 0.000 |
| 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