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Probabilistic evaluation of handwriting evidence: likelihood ratio for authorship

Author

Listed:
  • Silvia Bozza
  • Franco Taroni
  • Raymond Marquis
  • Matthieu Schmittbuhl

Abstract

Summary. The evaluation of handwritten characters that are selected from an anonymous letter and written material from a suspect is an open problem in forensic science. The individualization of handwriting is largely dependent on examiners who evaluate the characteristics in a qualitative and subjective way. Precise individual characterization of the shape of handwritten characters is possible through Fourier analysis: each handwritten character can be described through a set of variables such as the surface and harmonics as demonstrated by Marquis and co‐workers in 2005. The assessment of the value of the evidence is performed through the derivation of a likelihood ratio for multivariate data. The methodology allows the forensic scientist to take into account the correlation between variables, and the non‐constant variability within sources (i.e. individuals). Numerical procedures are implemented to handle the complexity and to compute the marginal likelihood under competing propositions.

Suggested Citation

  • Silvia Bozza & Franco Taroni & Raymond Marquis & Matthieu Schmittbuhl, 2008. "Probabilistic evaluation of handwriting evidence: likelihood ratio for authorship," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(3), pages 329-341, June.
    • Handle: RePEc:bla:jorssc:v:57:y:2008:i:3:p:329-341
    DOI: 10.1111/j.1467-9876.2007.00616.x
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    Cited by:

    1. Ivo Alberink & Annabel Bolck & Sonja Menges, 2013. "Posterior likelihood ratios for evaluation of forensic trace evidence given a two-level model on the data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(12), pages 2579-2600, December.
    2. Jan Hannig & Hari Iyer, 2022. "Testing for calibration discrepancy of reported likelihood ratios in forensic science," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 267-301, January.
    3. Christopher Galbraith & Padhraic Smyth & Hal S. Stern, 2020. "Quantifying the association between discrete event time series with applications to digital forensics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1005-1027, June.
    4. Jonathan J. Koehler, 2011. "If the Shoe Fits They Might Acquit: The Value of Forensic Science Testimony," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 8(s1), pages 21-48, December.

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