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ROC curves for different signal strengths

ROC curves for regression

ROC curves for regression

... ‘canonical’ ROC space for regression, corresponding exactly to the ROC space for classifica- tion, since regression and classification are very different ...space, curves and analysis that we ...

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Lehmann Family of ROC Curves

Lehmann Family of ROC Curves

... the ROC Curves of Several Markers The comparison of two markers is an important case of covariate ...use different statistical techniques to make predictions such as logistic regression, ...

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ROC curves in cost space

ROC curves in cost space

... rate that is between two split points of a ranking, we randomly choose between the split points in such a way that the desired rate is achieved in expectation. Figure 1 (left) illustrates this graphically. Example 1 ...

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A review on ROC curves in the presence of covariates

A review on ROC curves in the presence of covariates

... the ROC analysis by means of illustrating the consequences that ignoring such information may have on the practical conclusions drawn from the study at ...two different scenarios on which covariate ...

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Consistency Results for the ROC Curves of Fused Classifiers

Consistency Results for the ROC Curves of Fused Classifiers

... There are many advantages to MCSs. For example, each individual classifier system can focus on a different type of feature data, such as length or temperature gradient. They can also be specifically trained to classify ...

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Nonparametric and semiparametric  inference about ROC curves

Nonparametric and semiparametric inference about ROC curves

... The ROC curve is defined as ...in ROC formulation. The ROC curve can be plotted by pairs (P (X > c t ), P (Y > c t )), c t ∈ R ; that is, a plot of true positive fraction (TPF) versus false positive ...

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Survival Model Predictive Accuracy and ROC Curves

Survival Model Predictive Accuracy and ROC Curves

... 2.1 Extensions of Sensitivity and Specificity For survival data there are several potential extensions of cross-sectional sensitivity and specificity. Rather than a simple binary outcome, Y i = 1, a survival time can be ...

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Comparing ROC Curves Derived From Regression Models

Comparing ROC Curves Derived From Regression Models

... It is instructive to examine graphically the way the bias in the mean of the AUC test statistic operates. Figure 2 plots the standardized Wald test statistic against the AUC test statistic for two scenarios: no ...

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Relating ROC and CMC Curves via the Biometric Menagerie

Relating ROC and CMC Curves via the Biometric Menagerie

... the ROC curve and CMC curve, it is clear that neither model com- pletely predicted the empirical CMC curve based solely on the ROC ...which different individuals contribute towards the overall ...

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Diagnostic methods 2: receiver operating characteristic (ROC) curves

Diagnostic methods 2: receiver operating characteristic (ROC) curves

... serve different purposes like screening, diagnosis, and ...Furthermore, ROC curve analysis allows direct comparison of the accuracy of two or more quantitative tests for the same disease/condition and it ...

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Automatic misclassification rejection for LDA classifier using ROC curves

Automatic misclassification rejection for LDA classifier using ROC curves

... II. M ETHODS A. Data Acquisition Eight trans-radial amputees (including three congenital) and five partial hand amputees participated in an experimental study, which was approved by the University of Strathclyde Ethics ...

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Smoothed Empirical Likelihood Inference for ROC Curves with Missing Data

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

An Improved Method for Bandwidth Selection when Estimating ROC Curves

... when estimating ROC curves Peter G. Hall 1 and Rob J. Hyndman 1,2 13 September 2002 Abstract: The receiver operating characteristic (ROC) curve is used to describe the performance of a diagnostic ...

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Non-Parametric Estimation of ROC Curves in the Absence of a Gold Standard

Non-Parametric Estimation of ROC Curves in the Absence of a Gold Standard

... Table 5 lists the MLEs from both parametric and non-parametric ap- proaches when the data are actual binormal. The data were generated from two overlapping Gaussian distributions with different degree of ...

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Semiparametric Estimation of Time-Dependent:  ROC Curves for Longitudinal Marker Data

Semiparametric Estimation of Time-Dependent: ROC Curves for Longitudinal Marker Data

... Recent developments in molecular technology such as gene-expression microarrays and proteomics have led to a surge in research aimed at discovering markers useful for disease screening or for providing patient prognosis. ...

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Semiparametric Inferential Procedures for Comparing Multivariate ROC Curves with Interaction Terms

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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Application of the Time-Dependent ROC Curves for Prognostic Accuracy with Multiple Biomarkers

Application of the Time-Dependent ROC Curves for Prognostic Accuracy with Multiple Biomarkers

... underlying object function under certain condition, however their theoretical merits in regard to optimality are difficult to evaluate. Therefore the time varying logistic regression can be used as a benchmark for ...

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Adaptation of the weighted Kaplan-Meier method to time-dependent  ROC curves

Adaptation of the weighted Kaplan-Meier method to time-dependent ROC curves

... time-dependent ROC curve analysis. The performances of these two time-dependent ROC curve methods were compared, in which the Kaplan-Meier estimator and weighted Kaplan-Meier estimator were ...for ...

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A Kriging Algorithm for Fingerprinting Positioning with received Signal Strengths

A Kriging Algorithm for Fingerprinting Positioning with received Signal Strengths

... V. C ONCLUSIONS AND F UTURE W ORK In this paper, the Kriging algorithm, which is a statistics- based spatial interpolation algorithm, is proposed to construct RSS fingerprint database with sparsely collected measure- ...

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Tracking target signal strengths on a grid using sparsity

Tracking target signal strengths on a grid using sparsity

... Figure 8 WD versus time for sparsity-agnostic and sparsity-aware TSSG-KF and TSSG-IEKF trackers. accuracy of the TSS map provided by the TSSG fil- ters is essential in ensuring good performance of posi- tion estimates ...

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