Title: Robust Model-Based Clustering of Voting Records,
Abstract: We explore the possibility of discovering extreme voting patterns in the U.S.
Congressional voting records by drawing ideas from the mixture of contaminated
normal distributions. A mixture of latent trait models via contaminated normal
distributions is proposed. We assume that the low dimensional continuous latent
variable comes from a contaminated normal distribution and, therefore, picks up
extreme patterns in the observed binary data while clustering. We consider in
particular such model for the analysis of voting records. The model is applied
to a U.S. Congressional Voting data set on 16 issues. Note this approach is the
first instance within the literature of a mixture model handling binary data
with possible extreme patterns.