Order-restricted inference for clustered ROC data with application to fingerprint matching accuracy
文献类型：期刊论文
| 作者 | Zhang, Wei4; Tang, Larry L.2; Li, Qizhai4; Liu, Aiyi3; Lee, Mei-Ling Ting1 |
| 刊名 | BIOMETRICS
 |
| 出版日期 | 2019-12-04 |
| 页码 | 11 |
| 关键词 | area under the ROC curve clustered data fingerprint identification ROC curve stochastic ordering |
| ISSN号 | 0006-341X |
| DOI | 10.1111/biom.13177 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000500372200001 |
| 出版者 | WILEY |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/50365]  |
| 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.Univ Maryland, Dept Epidemiol & Biostat, College Pk, MD 20742 USA 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.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, LSC, Beijing, Peoples R China |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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