  In [Lavielle and Ludena 07], a random thresholding metho d is intro duced to
select the significant, or non null, mean terms among a collection of
independent random variables, and applied to the problem of recovering the
significant coefficients in non ordered model selection. We intro duce a simple
modification which removes the dep endency of the proposed estimator on a
window parameter while maintaining its asymptotic properties. A simulation
study suggests that both procedures compare favorably to standard thresholding
approaches, such as multiple testing or model-based clustering, in terms of the
binary classification risk. An application of the method to the problem of
activation detection on functional magnetic resonance imaging (fMRI) data is
discussed.
