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A Covariate-Adjusted Homogeneity Test with Application to Facial Recognition Accuracy Assessment

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arxiv 2307.08846 v1 pith:YHMHA7M3 submitted 2023-07-17 stat.AP stat.MEstat.ML

A Covariate-Adjusted Homogeneity Test with Application to Facial Recognition Accuracy Assessment

classification stat.AP stat.MEstat.ML
keywords teststudiesaccuracyproposedcovariate-adjusteddifferencesgroupshomogeneity
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Ordinal scores occur commonly in medical imaging studies and in black-box forensic studies \citep{Phillips:2018}. To assess the accuracy of raters in the studies, one needs to estimate the receiver operating characteristic (ROC) curve while accounting for covariates of raters. In this paper, we propose a covariate-adjusted homogeneity test to determine differences in accuracy among multiple rater groups. We derived the theoretical results of the proposed test and conducted extensive simulation studies to evaluate the finite sample performance of the proposed test. Our proposed test is applied to a face recognition study to identify statistically significant differences among five participant groups.

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