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The likelihood ratio as a random variable, with applications to DNA mixtures and kinship analysis

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FOSW03 - Statistical Modelling of Scientific Evidence

In forensic genetics, as in other areas of forensics, the data to be statistically evaluated are the result of a chance process, and hence one may conceive that they had been different, resulting in another likelihood ratio. Thus, one thinks of the obtained likelihood ratio in the case at hand as the outcome of a random variable. In this talk we will discuss the way to formalize this intuitive notion, and show general properties that the resulting distributions of the LR must have. We illustrate and apply these general results both to the evaluation of DNA mixtures and to kinship analysis, two standard applications of forensic DNA profiles. For mixtures, we discuss how model validation can be aided by investigation of the obtained likelihood ratios. For kinship analysis we observe that for any pairwise kinship comparison, the expected likelihood ratio does not depend on the allele frequencies of the loci that are used other than through the total number of alleles. We compare the behavior of the LR as a function of the allele frequencies with that of the weight of evidence, Log(LR), and argue that the WoE is better behaved. This talk is largely based on a series of three papers in International Journal of Legal Medicine co-authored with Thore Egeland.

Exclusion probabilities and likelihood ratios with applications to kinship problems, Int. J. Legal Med. 128, 2014, 415—-425,
Exclusion probabilities and  likelihood ratios with applications to mixtures, Int. J. Legal Med. 130, 2016, 39—-57,
The likelihood ratio as a random variable for linked markers in kinship analysis, Int. J. Legal Med. 130, 2016, 1445—-1456

This talk is part of the Isaac Newton Institute Seminar Series series.

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