ROC curves (plotting sensitivity against 1-‐specificity) constructed for each index
ROC curves for regression
... Figure 1 but calculated on the deployment ...for each cost function [11, ...RROC curves (even if we use cross-validation or confidence ...RROC curves, but inherited from the very nature of ...
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ROC curves in cost space
... to each positive instance and a probability 1 to each negative instance creating a group for each ...at each iteration, for “adjacent violators”: adjacent groups whose probabilities ...
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Lehmann Family of ROC Curves
... the ROC Curves of Several Markers The comparison of two markers is an important case of covariate ...an index such as PSA velocity, is a better predictor of disease ...
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A review on ROC curves in the presence of covariates
... the ROC curve in order to make use of the additional ...the ROC curve might be done for two purposes: (a) obtain covariate-specific ROC curves, or ROC curves that condition on a ...
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Consistency Results for the ROC Curves of Fused Classifiers
... the ROC curve of this new fused classifier. If the ROC curve for each individual classifier has been determined, then formulas for the ROC curve of the fused classifier exist for certain ...
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Nonparametric and semiparametric inference about ROC curves
... the ROC curve is the existence of a gold standard for the truth, which means that the true disease status of each patient is known by some other independent accurate diagnostic ...The ROC curve can ...
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Using ROC curves to choose minimally important change thresholds when sensitivity and specificity are valued equally : the forgotten lesson of Pythagoras : theoretical considerations and an example application of change in health status
... in each case, an infinite number of lines exist and we have rendered only some of these for the sake of ...of ROC space, but that each possible point on contours described by the sum of squares ...
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Comparing ROC Curves Derived From Regression Models
... W 1 and Z then the sign of β ˜ 2 will usually be ...is constructed under the assumption that half the time the results should lead to a decrement in AUC, but in fact this rarely ...
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Relating ROC and CMC Curves via the Biometric Menagerie
... Although the data in Figure 2 demonstrates that there may be some degree of “correlation” between the ROC curve and CMC curve, it is clear that neither model com- pletely predicted the empirical CMC curve based ...
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Smoothed Empirical Likelihood Inference for ROC Curves with Missing Data
... Received September 25, 2011; revised November 4, 2011; accepted November 16, 2011 ABSTRACT The receiver operating characteristic (ROC) curve has been widely used in scientific research fields. After using the ...
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An Improved Method for Bandwidth Selection when Estimating ROC Curves
... estimating ROC curves Peter G. Hall 1 and Rob ...of each group of observations and gives substantial improvement in MISE over other proposed methods, especially when the two distributions are ...
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Non-Parametric Estimation of ROC Curves in the Absence of a Gold Standard
... at each iteration of the EM algorithm the parameters are being updated in such a manner as to always increase the log-likelihood and mean- while the sum of squared residuals remains ...
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Automatic misclassification rejection for LDA classifier using ROC curves
... Subsequently, ROC analysis is applied for each gesture, generating the ROC ...which ROC patterns not passing the threshold check are ...Table 1. However, in a successful classification ...
5
Adaptation of the weighted Kaplan-Meier method to time-dependent ROC curves
... Simulation strategy used by Heagerty and Zheng (2005) ZDVLPSOHPHQWHG7RFUHDWHWKHµWUXH¶52&FXUYHIDOVH SRVLWLYH )3 YDOXHVZHUH¿[HGWREH« and true positive (TP) values were estimated at these FP ...
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Semiparametric Estimation of Time-Dependent: ROC Curves for Longitudinal Marker Data
... For each of the observed cases, we assign two matched controls by selecting subjects who had been followed for more than 5 years and who are still alive at the end of the ...
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Semiparametric Inferential Procedures for Comparing Multivariate ROC Curves with Interaction Terms
... Multivariate ROC curve models that include an interaction term be- tween biomarker type and false positive rate is important in comparative biomarker studies, because such interaction allows ROC ...
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Statistical Inference on Trimmed Means, Lorenz Curves, and Partial Area Under ROC Curves by Empirical Likelihood Method
... Lorenz Curves could include negative income, we excluded one record with HHINCOME = -9999 from Indiana since this negative number is most likely a coded ...Lorenz Curves and 95% confidence intervals at ...
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Sensitivity and Specificity of Preschool Vision Screening in Iran
... (ROC) curves and calculated the area under the curves in the present ...in each city was not proportionate to the total num- ber of students in that city, the weight of each city in ...
9
Sensitivity, Specificity, ROC. Multiple testing. Independent filtering
... One way to do it properly is to make sure that stage-one (filter) and stage-two (differential expression) statistics are marginally independent:. (Normal distributed data): overall var[r] ...
Survival Model Predictive Accuracy and ROC Curves
... an ROC curve that plots the “true positive rate” (sensitivity) versus the “false positive rate” (1-specifity) for all c ∈ (−∞, ...An ROC curve provides complete information on the set of all ...
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