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@misc{864240,
author = {Koláček, Jan},
booktitle = {QPRC 2009 New York},
keywords = {ROC curve; kernel estimation; reflection; distribution estimation},
language = {eng},
title = {ROC curves as an aspect of classification},
url = {http://research.ihost.com/qprc_2009/},
year = {2009}
}
TY - SLIDE
ID - 864240
AU - Koláček, Jan
PY - 2009
TI - ROC curves as an aspect of classification
KW - ROC curve
KW - kernel estimation
KW - reflection
KW - distribution estimation
UR - http://research.ihost.com/qprc_2009/
N2 - Receiver Operating Characteristic (ROC) analysis has its origin in signal detection theory, but most of the current work occurs in the medical decision making community. Now, ROC curves have been widely used for evaluating the accuracy and discriminating power of a diagnostic test or statistical model. To derive a smooth estimate for the ROC curve, we use a kernel smoothing method. We estimate a distribution function by this process. It is well known now that kernel distribution estimators are not consistent when estimating a distribution near the finite end points of its support. This is due to boundary effects that occur in nonparametric curve estimation problems. To avoid these difficulties we use the technique, which is a kind of generalized reflection method involving reflecting a transformation of the data.
ER -
KOLÁČEK, Jan. ROC curves as an aspect of classification. In \textit{QPRC 2009 New York}. 2009.
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