Statistical matching rules and applications for common source identification
Date
2024-05
Authors
Lee, Hana
Major Professor
Advisor
Qiu, Yumou
Carriquiry, Alicia
Ommen, Danica
Hofmann, Heike
Liu, Peng
Committee Member
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Altmetrics
Abstract
We propose several statistical matching rules to address common source identification problems in forensic science, where a key question is determining whether two items originated from the same source. A statistical matching rule denotes a statistical decision-making method for source identification problems. In Chapter 2, we derive a parametric matching rule that minimizes a weighted sum of error probabilities under known density functions in the closed-set framework. In Chapter 3, we introduce a method to automatically align two similar footwear impressions and to identify the common source. In Chapter 4, we propose a nonparametric matching rule that leverages statistical classification. We showcase the performance of our proposed methods through numerical simulations or on various datasets in comparison to existing ones.
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Type
dissertation