Economically Optimal Design of a Multivariate Synthetic<i>T</i><sup>2</sup>Chart
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Economic and economic-statistical models are developed for the synthetic T 2 chart. The input parameters that result in larger cost and affect the optimal parameters are identified. The optimal parameters are quite robust toward changes in input parameters, except the number of variables and the Mahalanobis distance. Alternative choices of parameters, which result in minimal cost increase, can be chosen if it is infeasible to operate the chart optimally. The results are based on numerical examples and verified through simulation. The synthetic T 2 chart has better economic and economic-statistical performances than the Hotelling's T 2 and MEWMA charts under most conditions. Keywords: Comparison of economic performanceCost minimization, MisspecificationMultivariate synthetic T 2 chartOptimal parametersSensitivity analysesMathematical Subject Classification: 62P3090C31 Acknowledgments This work is supported by the Universiti Sains Malaysia, Fundamental Research Grant Scheme (FRGS), no. 203/PMATHS/6711232.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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 | 0.001 | 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