Proposes Dirichlet process mixtures of block g priors that enable differential shrinkage on data-selected blocks of correlated predictors in linear models, with consistency guarantees and an MCMC sampler.
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Dirichlet process mixtures of block $g$ priors for model selection and prediction in linear models
Proposes Dirichlet process mixtures of block g priors that enable differential shrinkage on data-selected blocks of correlated predictors in linear models, with consistency guarantees and an MCMC sampler.