  The empirical likelihood is a powerful nonparametric tool, that emulates its
parametric counterpart -- the parametric likelihood -- preserving many of its
large-sample properties. This article tackles the problem of assessing the
discriminatory power of three-class diagnostic tests from an empirical
likelihood perspective. In particular, we concentrate on interval estimation in
a three-class ROC analysis, where a variety of inferential tasks could be of
interest. We present novel theoretical results and tailored techniques studied
to efficiently solve some of such tasks. Extensive simulation experiments are
provided in a supporting role, with our novel proposals compared to existing
competitors, when possible. It emerges that our new proposals are extremely
flexible, being able to compete with contestants and being the most suited to
accommodating flexible distributions for target populations. We illustrate the
application of the novel proposals with a real data example. The article ends
with a discussion and a presentation of some directions for future research.
