Fig. 2.

Bias magnitude and relative error of AUC estimators as a function of the total number of images, 2n. The plots assume p = 40 channels with AUC = 0.6 (left), 0.75 (middle), and 0.9 (right). Bias magnitude is on the top and relative error is on the bottom. The estimators are ${\widehat{\text{AUC}}}_1$ (circles), ${\widehat{\text{AUC}}}_2$ (diamonds), ${\widehat{\text{AUC}}}_3$ (triangles), ${\widehat{\text{AUC}}}_4$ (squares). For each i ∈ {1, 2, 3, 4}, ${\widehat{\text{AUC}}}_i$ is calculated by substituting the square root of θ̂_i for SNR in equation (5). The bias magnitude (in %) for an AUC estimator, ${\widehat{\text{AUC}}}_i$, is calculated as $(\mid \text{E}[{\widehat{\text{AUC}}}_i]-\text{AUC}\mid /\text{AUC})\times 100$. The relative error (in %) is calculated as $(\sqrt{\text{MSE}}/\text{AUC})\times 100$, where MSE denotes the mean-square error.