Performance of High Breakdown Mixture Discriminant Analysis Under Different Biweight Functions
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Bibliographic record
Abstract
Abstract This article addresses the use of different biweight functions in high breakdown mixture discriminant analysis approach of Bashir and Carter (Citation2005). The translated biweight S function of Rocke (Citation1996) is used in the robust estimation of the mixture distributions parameters. As in discriminant analysis, the main purpose is to classify the test observations with greater accuracy, so the estimators producing smaller errors of misclassification are preferred. In the simulation studies, the use of Tukey's biweight function was proved to be more effective in producing better classification results as compared to the translated biweight function. Keywords: Asymptotic rejection probabilityBreakdown pointMixture models S-EstimatorsTranslated biweight functionMathematics Subject Classification: Primary 62F35Secondary 62H30
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 |
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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