Order-restricted inference for clustered ROC data with application to fingerprint matching accuracy

doi:10.1111/biom.13177

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	Order-restricted inference for clustered ROC data with application to fingerprint matching accuracy
	Zhang, Wei 1; Tang, Larry L.2; Li, Qizhai 1; Liu, Aiyi 3; Lee, Mei-Ling Ting 4
	2019-12-04
发表期刊	BIOMETRICS
ISSN	0006-341X
页码	11
摘要	Receiver 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.
关键词	area under the ROC curve clustered data fingerprint identification ROC curve stochastic ordering
DOI	10.1111/biom.13177
收录类别	SCI
语种	英语
资助项目	National Institute of Justice[2018-DU-BX-0228] ; National Institutes of Health[R01EY02445]
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biology ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000500372200001
出版者	WILEY
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/50366
专题	中国科学院数学与系统科学研究院
通讯作者	Tang, Larry L.
作者单位	1.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
推荐引用方式 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.