Please use this identifier to cite or link to this item: https://doi.org/10.3109/1354750X.2013.868516
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dc.titleSorting multiple classes in multi-dimensional ROC analysis: Parametric and nonparametric approaches
dc.contributor.authorLi, J.
dc.contributor.authorChow, Y.
dc.contributor.authorWong, W.K.
dc.contributor.authorWong, T.Y.
dc.date.accessioned2014-10-28T05:17:03Z
dc.date.available2014-10-28T05:17:03Z
dc.date.issued2014-02
dc.identifier.citationLi, J., Chow, Y., Wong, W.K., Wong, T.Y. (2014-02). Sorting multiple classes in multi-dimensional ROC analysis: Parametric and nonparametric approaches. Biomarkers 19 (1) : 1-8. ScholarBank@NUS Repository. https://doi.org/10.3109/1354750X.2013.868516
dc.identifier.issn1354750X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105504
dc.description.abstractIn large-scale data analysis, such as in a microarray study to identify the most differentially expressed genes, diagnostic tests are frequently used to classify and predict subjects into their different categories. Frequently, these categories do not have an intrinsic natural order even though the quantitative test results have a relative order. As identifying the correct order for a proper definition of accuracy measures is important for a high-dimensional receiver operating characteristic (ROC) analysis, we propose rigorous and automated approaches to sort out the multiple categories using simple summary statistics such as means and relative effects. We discuss the hypervolume under the ROC manifold (HUM), its dependence on the order of the test results and the minimum acceptable HUM values in a general multi-category classification problem. Using a leukemia data set and a liver cancer data set, we show how our approaches provide accurate screening results when we have a large number of tests. © 2014 Informa UK Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.3109/1354750X.2013.868516
dc.sourceScopus
dc.subjectHypervolume under the ROC manifold
dc.subjectKruskal-Wallis test
dc.subjectMultivariate normal distribution
dc.subjectRelative effects
dc.subjectVolume under the ROC surface
dc.typeReview
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.3109/1354750X.2013.868516
dc.description.sourcetitleBiomarkers
dc.description.volume19
dc.description.issue1
dc.description.page1-8
dc.description.codenBIOMF
dc.identifier.isiut000330885100001
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