Salīdzināt metodes

	Robust ROC analīze	Robusta efektu lieluma analīze
Nozare	Statistika	Statistika
Saime	Hypothesis test	Hypothesis test
Izcelsmes gads	1990s–2000s	2005 (formalized)
Autors	Multiple contributors (Pepe, Qin, Zhou, and others)	Algina, Keselman & Penfield; Wilcox
Tips	Robust diagnostic accuracy evaluation	Robust effect size estimation
Pirmavots	Pepe, M. S. (2000). An interpretation for the ROC curve and inference using GLM procedures. Biometrics, 56(2), 352–359.	Algina, J., Keselman, H. J., & Penfield, R. D. (2005). An alternative to Cohen's standardized mean difference effect size: A robust parameter and confidence interval in the two independent groups case. Psychological Methods, 10(3), 317–328.
Citi nosaukumi	robust AUC analysis, outlier-resistant ROC, robust diagnostic accuracy analysis, robust sensitivity-specificity analysis	robust Cohen's d, trimmed-mean effect size, outlier-resistant effect size, robust standardized mean difference
Saistītās	3	5
Kopsavilkums	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.	Robust effect size analysis quantifies the magnitude of a difference or association using estimators that are resistant to outliers and violations of normality. Rather than relying on classical statistics such as Cohen's d based on sample means and standard deviations, robust variants use trimmed means and Winsorized standard deviations to produce effect size estimates that accurately reflect the typical effect rather than being inflated by extreme values.