Assessment of Approximate Likelihood Ratios from Continuous Distributions: A Case Study of Digital Camera Identification

Authors

  • Anders Nordgaard Ph.D.,

    1. The Swedish National Laboratory of Forensic Science, SE-581 94 Linköping, Sweden.
    2. Department of Computer and Information Science, Linköping University, SE-581 83 Linköping, Sweden.
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  • Tobias Höglund M.Sc.

    1. The Swedish National Laboratory of Forensic Science, SE-581 94 Linköping, Sweden.
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  • Presented in part at the Seventh International Conference on Forensic Inference and Statistics, August 20–23, 2008, in Lausanne, Switzerland.

Additional information and reprint requests:
Anders Nordgaard, Ph.D.
The Swedish National Laboratory of Forensic Science
SE-581 94 Linköping
Sweden
E-mail: Anders.Nordgaard@liu.se

Abstract

Abstract:  A reported likelihood ratio for the value of evidence is very often a point estimate based on various types of reference data. When presented in court, such frequentist likelihood ratio gets a higher scientific value if it is accompanied by an error bound. This becomes particularly important when the magnitude of the likelihood ratio is modest and thus is giving less support for the forwarded proposition. Here, we investigate methods for error bound estimation for the specific case of digital camera identification. The underlying probability distributions are continuous and previously proposed models for those are used, but the derived methodology is otherwise general. Both asymptotic and resampling distributions are applied in combination with different types of point estimators. The results show that resampling is preferable for assessment based on asymptotic distributions. Further, assessment of parametric estimators is superior to evaluation of kernel estimators when background data are limited.

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