A new method of kernel-smoothing estimation of the ROC curve

Michał Pulit ()
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Michał Pulit: Wrocław University of Technology

Metrika: International Journal for Theoretical and Applied Statistics, 2016, vol. 79, issue 5, No 6, 603-634

Abstract: Abstract The receiver operating characteristic (ROC) curve is a popular graphical tool for describing the accuracy of a diagnostic test. Based on the idea of estimating the ROC curve as a distribution function, we propose a new kernel smoothing estimator of the ROC curve which is invariant under nondecreasing data transformations. We prove that the estimator has better asymptotic mean squared error properties than some other estimators involving kernel smoothing and we present an easy method of bandwidth selection. By simulation studies, we show that for the limited sample sizes, our proposed estimator is competitive with some other nonparametric estimators of the ROC curve. We also give an example of applying the estimator to a real data set.

Keywords: ROC curve; Nonparametric estimation; Kernel smoothing; Bandwidth selection; 62G05; 62G20 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00184-015-0569-1

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