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Inferring effective brain connectivity from neuroimaging data such as functional Magnetic Resonance Imaging (fMRI) has been attracting increasing interest due to its critical role in understanding brain functioning. Incorporating sparsity into connectivity modeling to make models more biologically realistic and performing group analysis to deal with inter-subject variability are still challenges associated...
The Huberized LASSO model, a robust version of the popular LASSO, yields robust model selection in sparse linear regression. Though its superior performance was empirically demonstrated for large variance noise, currently no theoretical asymptotic analysis has been derived for the Huberized LASSO estimator. Here we prove that the Huberized LASSO estimator is consistent and asymptotically normal distributed...
In the context of linear regression, the least absolute shrinkage and selection operator (LASSO) is probably the most popular supervised-learning technique proposed to recover sparse signals from high-dimensional measurements. Prior literature has mainly concerned itself with independent, identically distributed noise with moderate variance. In many real applications, however, the measurement errors...
Sparsity is crucial for high-dimensional statistical modeling. On one hand, dimensionality reduction can reduce the variability of estimation and thus provide reliable predictive power. On the other hand, the selected sub-model can discover and emphasize the underlying dependencies, which is useful for objective interpretation. Many variable selection methods have been proposed in literatures. For...
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