In this paper researchers have examined five possible models for the behavior of the stutter ratio, SR.
These were two log-normal models, two gamma models, and a two-component normal mixture model. A two-component normal mixture model was chosen with different behaviors of variance; at each locus SR was described with two distributions, both with the same mean. The distributions have difference variances: one for the majority of the observations and a second for the less well-behaved ones. The researchers applied each model to a set of known single source Identifiler, NGM SElect and PowerPlex 21 DNA profiles to show the applicability of the findings to different datasets. SR determined from the single source profiles were compared to the calculated SR after application of the models. The model performance was tested by calculating the log-likelihoods and comparing the difference in Akaike information criterion (AIC). The two-component normal mixture model systematically outperformed all others, despite the increase in the number of parameters. This model, as well as performing well statistically have intuitive appeal for forensic biologists and could be implemented in an expert system with a continuous method for DNA interpretation. (Published Abstract)
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