Non Parametric Confidence Intervals for Receiver Operating Characteristic Curves

Peter G. Hall, Rob Hyndman and Yanan Fan

No 12/03, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We study methods for constructing confidence intervals, and confidence bands, for estimators of receiver operating characteristics. Particular emphasis is placed on the way in which smoothing should be implemented, when estimating either the characteristic itself or its variance. We show that substantial undersmoothing is necessary if coverage properties are not to be impaired. A theoretical analysis of the problem suggests an empirical, plug-in rule for bandwidth choice, optimising the coverage accuracy of interval estimators. The performance of this approach is explored. Our preferred technique is based on asymptotic approximation, rather than a more sophisticated approach using the bootstrap, since the latter requires a multiplicity of smoothing parameters all of which must be chosen in nonstandard ways. It is shown that the asymptotic method can give very good performance.

Keywords: Bandwidth selection; binary classification; kernel estimator; receiver operating characteristic curve. (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2003-07
New Economics Papers: this item is included in nep-cmp and nep-ecm
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Citations: View citations in EconPapers (6)

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