Bayesian sparse projection posteriors for high-dimensional grouped regression achieve optimal contraction rates and model selection consistency with applications to additive models and neuroimaging.
Since group sparsity is a special case of the general sparse high-dimensional linear regression problem, the same map used in Pal and Ghoshal
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Bayesian High-dimensional Grouped-regression using Sparse Projection-posterior
Bayesian sparse projection posteriors for high-dimensional grouped regression achieve optimal contraction rates and model selection consistency with applications to additive models and neuroimaging.