A rotation-based feature and Bayesian hierarchical model for the forensic evaluation of handwriting evidence in a closed set

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2023-06
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Crawford, Amy M.
Ommen, Danica M.
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© Institute of Mathematical Statistics, 2023
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Carriquiry, Alicia
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Center for Statistics and Applications in Forensic Evidence
The Center for Statistics and Applications in Forensic Evidence (CSAFE) carries out research on the scientific foundations of forensic methods, develops novel statistical methods and transfers knowledge and technological innovations to the forensic science community. We collaborate with more than 80 researchers and across six universities to drive solutions to support our forensic community partners with accessible tools, open-source databases and educational opportunities.
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Statistics

The Department of Statistics seeks to teach students in the theory and methodology of statistics and statistical analysis, preparing its students for entry-level work in business, industry, commerce, government, or academia.

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The Department of Statistics was formed in 1948, emerging from the functions performed at the Statistics Laboratory. Originally included in the College of Sciences and Humanities, in 1971 it became co-directed with the College of Agriculture.

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1948-present

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Center for Statistics and Applications in Forensic EvidenceStatistics
Abstract
Forensic handwriting examiners are often tasked with identifying the writer of a particular document. Examples of handwriting evidence include ransom notes, forged documents and signatures, and threatening letters. At present, examiners rely on visual inspection of similarities and differences between the questioned document and reference writing samples. Here, we propose a principled modeling approach to compute the posterior predictive probability of writership when the author of the questioned document is part of a closed set of writers. Given a handwritten document, we extract document-level and character-level measurements which are the response variables in a multi-level model. We fit the model and test its posterior predictive performance using writing samples from the United States and from Europe.We find that as long as the questioned document is longer than a sentence or two, it is possible to correctly associate a writer with a document that he or she wrote with high probability. Earlier versions of this work have been well received by the community of forensic document examiners.
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This is a manuscript of an article published as Amy M. Crawford. Danica M. Ommen. Alicia L. Carriquiry. "A rotation-based feature and Bayesian hierarchical model for the forensic evaluation of handwriting evidence in a closed set." Ann. Appl. Stat. 17 (2) 1127 - 1151, June 2023. https://doi.org/10.1214/22-AOAS1662. Posted with permission of CSAFE.
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