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                            Library & Multimedia
 Order-restricted inference for clustered ROC data with application to fingerprint matching accuracy
NCJ Number
              303315

Author(s)
          W. Zhang;
                        et al
Date Published

2019
Annotation
              This article presents the receiver operating characteristic (ROC) curve as a commonly used resource to evaluate and compare the accuracy of classification methods or markers.
Abstract
              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, the authors 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. The authors 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.
      
  
          Additional Details
Grant Number(s)
        2018-DU-BX-0228
                                2018-DU-BX-4228


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Statistical Infrastructure for the Use of Error Rate Studies in the Interpretation of Forensic Evidence
                                Statistical Infrastructure for the Use of Error Rate Studies in the Interpretation of Forensic Evidence
    
  
  
Date Published: January 1, 2019
    
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                Cluster analysis
        Fingerprints
        Applied Research



  
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