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| Robust ROC 분석× | ROC 분석 (수신자 조작 특성)× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 1990s–2000s | 1954 (signal detection); 1982 (AUC formalization) |
| 창시자≠ | Multiple contributors (Pepe, Qin, Zhou, and others) | Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics) |
| 유형≠ | Robust diagnostic accuracy evaluation | Diagnostic accuracy evaluation |
| 원전≠ | Pepe, M. S. (2000). An interpretation for the ROC curve and inference using GLM procedures. Biometrics, 56(2), 352–359. DOI ↗ | Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36. DOI ↗ |
| 별칭 | robust AUC analysis, outlier-resistant ROC, robust diagnostic accuracy analysis, robust sensitivity-specificity analysis | ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis |
| 관련≠ | 3 | 4 |
| 요약≠ | Robust ROC analysis evaluates the diagnostic accuracy of a continuous or ordinal biomarker in distinguishing between two groups (e.g., diseased vs. healthy) while protecting against the distorting effects of outliers, non-normality, or distributional violations that can bias standard parametric ROC estimates and AUC confidence intervals. | ROC analysis evaluates how well a continuous or ordinal test variable discriminates between two binary outcome classes. By plotting the true positive rate (sensitivity) against the false positive rate (1 − specificity) across all decision thresholds, it produces a curve whose area under the curve (AUC) quantifies overall discriminative power, ranging from 0.5 (chance) to 1.0 (perfect discrimination). |
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