Comparison of bandwidth selection methods for kernel smoothing of ROC curves.

Abstract

In this paper we compared four non-parametric kernel smoothing methods for estimating an ROC curve based on a continuous-scale test. All four methods produced a smooth ROC curve of the test. The difference in these four methods lay with the way they chose their bandwidth parameters. To assess the relative performance of the four bandwidth selection methods, we conducted a simulation study using different underlying distributions, along with varied sample sizes. The results from our simulation study suggested that the kernel smoothing method originally proposed by Altman and Léger for estimation of the distribution function was the best choice for estimation of an ROC curve. We illustrated these methods with a real example.