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                            Library & Multimedia
 Order-Constrained ROC Regression With Application to Facial Recognition
NCJ Number
              304339
Journal

*Technometrics* Volume: 63 Issue: 3 Dated: 2021 Pages: 343-353
Author(s)
          X. Zhu;
                        et al
Date Published

2021
Length
              11 pages
Annotation
              This article considers modeling of receiver operating characteristic (ROC) curves using both the order constraint and covariates associated with each score, given that the latter (e.g., demographic characteristics of the underlying subjects) often have a substantial impact on discriminative accuracy.
Abstract
              The receiver operating characteristic (ROC) curve is widely used to assess discriminative accuracy of two groups based on a continuous score. In a variety of applications, the distributions of such scores across the two groups exhibit a stochastic ordering. Specific examples include calibrated biomarkers in medical diagnostics or the output of matching algorithms in biometric recognition. Incorporating stochastic ordering as an additional constraint into estimation can improve statistical efficiency. The proposed method is based on the indirect ROC regression approach using a location-scale model, and quadratic optimization is used to implement the order constraint. The statistical properties of the proposed order-constrained least squares estimator are studied. Based on the theoretical results developed herein, the authors deduce that the proposed estimator can achieve substantial reductions in mean squared error relative to its unconstrained counterpart. Simulation studies corroborate the superior performance of the proposed approach. Its practical usefulness is demonstrated in an application to face recognition data from the “Good, Bad, and Ugly” face challenge, a domain in which accounting for covariates has hardly been studied. (Publisher Abstract)
      
  
          Additional Details
Grant Number(s)
        2019-DU-BX-4011


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Accounting for Covariates in Forensic Error Rate Assessment and Evidence Interpretation
    
  
  
Date Published: January 1, 2021
    
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