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Order-restricted inference for clustered ROC data with application to fingerprint matching accuracy
Zhang, Wei1; Tang, Larry L.2; Li, Qizhai1; Liu, Aiyi3; Lee, Mei-Ling Ting4
2019-12-04
Source PublicationBIOMETRICS
ISSN0006-341X
Pages11
AbstractReceiver operating characteristic (ROC) curve is commonly used to evaluate and compare the accuracy of classification methods or markers. Estimating ROC curves has been an important problem in various fields including biometric recognition and diagnostic medicine. In real applications, classification markers are often developed under two or more ordered conditions, such that a natural stochastic ordering exists among the observations. Incorporating such a stochastic ordering into estimation can improve statistical efficiency (Davidov and Herman, 2012). In addition, clustered and correlated data arise when multiple measurements are gleaned from the same subject, making estimation of ROC curves complicated due to within-cluster correlations. In this article, we propose to model the ROC curve using a weighted empirical process to jointly account for the order constraint and within-cluster correlation structure. The algebraic properties of resulting summary statistics of the ROC curve such as its area and partial area are also studied. The algebraic expressions reduce to the ones by Davidov and Herman (2012) for independent observations. We derive asymptotic properties of the proposed order-restricted estimators and show that they have smaller mean-squared errors than the existing estimators. Simulation studies also demonstrate better performance of the newly proposed estimators over existing methods for finite samples. The proposed method is further exemplified with the fingerprint matching data from the National Institute of Standards and Technology Special Database 4.
Keywordarea under the ROC curve clustered data fingerprint identification ROC curve stochastic ordering
DOI10.1111/biom.13177
Indexed BySCI
Language英语
Funding ProjectNational Institute of Justice[2018-DU-BX-0228] ; National Institutes of Health[R01EY02445]
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiology ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000500372200001
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/50366
Collection中国科学院数学与系统科学研究院
Corresponding AuthorTang, Larry L.
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, LSC, Beijing, Peoples R China
2.Univ Cent Florida, Natl Ctr Forens Sci Dept Stat, Orlando, FL 32816 USA
3.Eunice Kennedy Shriver Natl Inst Child Hlth & Hum, Biostat & Bioinformat Branch, NIH, Bethesda, MD USA
4.Univ Maryland, Dept Epidemiol & Biostat, College Pk, MD 20742 USA
Recommended Citation
GB/T 7714
Zhang, Wei,Tang, Larry L.,Li, Qizhai,et al. Order-restricted inference for clustered ROC data with application to fingerprint matching accuracy[J]. BIOMETRICS,2019:11.
APA Zhang, Wei,Tang, Larry L.,Li, Qizhai,Liu, Aiyi,&Lee, Mei-Ling Ting.(2019).Order-restricted inference for clustered ROC data with application to fingerprint matching accuracy.BIOMETRICS,11.
MLA Zhang, Wei,et al."Order-restricted inference for clustered ROC data with application to fingerprint matching accuracy".BIOMETRICS (2019):11.
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