Bayesian Linear Regression for Multivariate Responses Under Group Sparsity

Understand

We study frequentist properties of a Bayesian high-dimensional multivariate linear regression model with correlated responses.

• The predictors are separated into many groups and the group structure is pre-determined.
• Two features of the model are unique: (i) group sparsity is imposed on the predictors.
• (ii) the covariance matrix is unknown and its dimensions can also be high.