Assessment of Approximate Likelihood Ratios from Continuous Distributions: A Case Study of Digital Camera Identification
2011 (English)In: Journal of Forensic Sciences, ISSN 0022-1198, E-ISSN 1556-4029, Vol. 56, no 2, 390-402 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2011. Vol. 56, no 2, 390-402 p.
forensic science, likelihood ratio, digital cameras, generalized Gaussian distribution, confidence intervals, bootstrap
Probability Theory and Statistics Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-68002DOI: 10.1111/j.1556-4029.2010.01665.xOAI: oai:DiVA.org:liu-68002DiVA: diva2:414896