Abstract
In this work we introduce a new statistical methodology for empirically examining the validity of model-based Likelihood Ratio (LR) systems by applying a general statistical inference approach called generalized fiducial inference.LR systems are gaining widespread acceptance in many forensic disciplines, especially in the interpretation of DNA evidence, in the form of probabilistic genotyping systems (PGS). These systems output a Bayes factor, commonly referred to as a likelihood ratio in forensic science applications. Methods for examining the validity of such systems is a topic of ongoing interest. In addition to summarizing existing approaches and developing our new approach, we illustrate the methods using the PROVEDIt dataset by examining LR values calculated with two PG software packages.
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