The Performance of the EWMA Median Chart in the Presence of Measurement Error
Why this work is in the frame
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Bibliographic record
Abstract
Measurement error always exists in quality control applications and may considerably affect the ability of control charts to detect an out-of-control situation. In this paper, we study the performance of the EWMA median chart using a Markov Chain approach with a linear covariate error model and a corrected formula for the distribution of the sample median. The performance is evaluated through the Average Run Length. Several figures and tables are presented and enumerated to show the statistical performance of the EWMA median control chart for different sources of measurement errors. The positive effect of taking multiple measurements for each item in a subgroup on the performance of the EWMA median chart is also investigated. An example illustrates the use of this study is introduced. The numerical simulation shows that the measurement errors have a negative influence on the proposed chart.
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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 | 0.004 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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