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Comparison of bandwidth selection methods for kernel smoothing of ROC curves
Author(s) -
Zhou XiaoHua,
Harezlak Jaroslaw
Publication year - 2002
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1156
Subject(s) - smoothing , kernel smoother , bandwidth (computing) , kernel (algebra) , receiver operating characteristic , mathematics , kernel density estimation , statistics , parametric statistics , sample size determination , selection (genetic algorithm) , computer science , kernel method , artificial intelligence , radial basis function kernel , support vector machine , computer network , combinatorics , estimator
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. Copyright © 2002 John Wiley & Sons, Ltd.