Evaluating score- and feature-based likelihood ratio models for multivariate continuous data: applied to forensic MDMA comparison
Law Probability and Risk September 1, 2015 Annabel Bolck, Haifang Ni, Martin Lopatka 51 citations
Likelihood ratio models are increasingly used in forensic science to evaluate evidence. When identical raw data are used, feature-based and score-based models produce fundamentally different results. Score-based models yield much lower absolute likelihood ratios and are more stable than feature-based models, because they reduce multivariate information to a univariate distance or similarity score. Feature-based models retain the full multivariate structure of original feature values. The paper explains how data pre-treatment and dimension reduction affect both methods, using chemical profiles of MDMA as an example.