by Wendy K. Tam Cho and George G. Judge

Journal of Data Science, v.6, no.2, 155-171

Abstract

In voting rights cases, judges often infer unobservable individual vote choices from election data aggregated at the precinct level. That is, one must solve an ill-posed inverse problem to obtain the critical information used in these cases. The ill-posed nature of the problem means that traditional frequentist and Bayesian approaches cannot be employed without first imposing a range of assumptions. In order to mitigate the problems resulting from incorporating potentially inaccurate information in these cases, we propose the use of information theoretic methods as a basis for recovering an estimate of the unobservable individual vote choices. We illustrate the empirical non-parametric likelihood methods with some election data.

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