  In hypothesis testing, the phenomenon of label noise, in which hypothesis
labels are switched at random, contaminates the likelihood functions. In this
paper, we develop a new method to determine the decision rule when we do not
have knowledge of the uncontaminated likelihoods and contamination
probabilities, but only have knowledge of the contaminated likelihoods. In
particular we pose a minimax optimization problem that finds a decision rule
robust against this lack of knowledge. The method simplifies by application of
linear programming theory. Motivation for this investigation is provided by
problems encountered in workforce analytics.
