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                            Funding & Awards
 Quantifying the Effects of Database Size and Sample Quality on Measures of Individualization Validity and Accuracy in Forensics
Award Information
Awardee
              George Mason University
Award #
              2009-DN-BX-K234
Funding Category
              Competitive
Location
                    Fairfax, VA
Congressional District
              11
Status
              Closed
Funding First Awarded
              2009
Total funding (to date)
              $974,981
Original Solicitation
              NIJ FY 09 Fundamental Research to Improve Understanding of the Accuracy, Reliability, and Measurement Validity of Forensic Science Disciplines
Description of original award (Fiscal Year 2009, $974,981)
The researchers propose to develop methods to statistically quantify (1) the uncertainty in measures aimed at validating a forensic discipline's basic premises (such as a uniqueness claim) and (2) the use of likelihood ratio methods in making classification/individualization conclusions. The use of automated pairwise comparisons of biometric samples in a database is a basic element of forensic individualization determinations involving biometrics, such as fingerprints and handwriting. An issue that applies to forensic individualization is that while a database of samples can be used to support individuality, it does not necessarily prove individuality. Phase I of this proposed project will focus on the random match probability (RMP), as a measure of the validity of a forensic individualization procedure. In Phases II and III, the researchers will shift focus to quantifying accuracy with likelihood ratio methods. The researchers propose to investigate its estimation in other fields outside of DNA, such as handwriting and glass fragments, focusing both on statistically sound point estimates and confidence intervals. Most of the methodologies developed in this proposed project will apply to any field of forensics as RMPs and likelihood ratios are defined similarly in many of them. 

ca/ncf



  
Grant-Funded Publications
        
                      U-statistics for estimating performance metrics in forensic handwriting analysis
                  Kernel-based Methods for Source Identification Using Very Small Particles From Carpet Fibers
                  Quantifying the Effects of Database Size and Sample Quality on Measures of Individualization Validity and Accuracy in Forensics
                  Using subsampling to estimate the strength of handwriting evidence via score-based likelihood ratios
                  Score-based likelihood ratios for handwriting evidence

  
Date Created: September 23, 2009
    
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