  The estimation of signal dimension under heavy-tailed latent factor models is
studied. As a primary contribution, robust extensions of an earlier estimator
based on Gaussian Stein's unbiased risk estimation are proposed. These novel
extensions are based on the framework of elliptical distributions and robust
scatter matrices. Extensive simulation studies are conducted in order to
compare the novel methods with several well-known competitors in both
estimation accuracy and computational speed. The novel methods are applied to a
financial asset return data set.
