Random threshold for linear model selection, revisited

In [11], a random thresholding method is introduced 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 nonordered model selection. We introduce a simple modification which removes the dependency 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.