Model distribution effects on likelihood ratios in fire debris analysis

Abstract

Computational models for determining the strength of fire debris evidence based on likelihood ratios (LR) were developed and validated against data sets derived from different distributions of ASTM E1618-14 designated ignitable liquid class and substrate pyrolysis contributions using in-silico generated data. The models all perform well in cross validation against the distributions used to generate the model. However, a model generated based on data that does not contain representatives from all of the ASTM E1618-14 classes does not perform well in validation with data sets that contain representatives from the missing classes. A quadratic discriminant model based on a balanced data set (ignitable liquid versus substrate pyrolysis), with a uniform distribution of the ASTM E1618-14 classes, performed well (receiver operating characteristic area under the curve of 0.836) when tested against laboratory-developed casework-relevant samples of known ground truth.