In multivariate regression analysis, reduced-rank regression (RRR) has received
considerable attention as a powerful way of improving estimation and prediction …

In the context of high-dimensional multivariate linear regression, sparse reduced-rank
regression (SRRR) provides a way to handle both variable selection and low-rank …

We develop a Bayesian methodology aimed at simultaneously estimating low-rank and row-
sparse matrices in a high-dimensional multiple-response linear regression model. We …

The reduced-rank regression is an effective method in predicting multiple response
variables from the same set of predictor variables. It reduces the number of model …

A reduced-rank regression with sparse singular value decomposition (RSSVD) approach
was proposed by Chen et al. for conducting variable selection in a reduced-rank model. To …

Many modern statistical problems can be cast in the framework of multivariate regression,
where the main task is to make statistical inference for a possibly sparse and low-rank …

We consider joint rank and variable selection in multivariate regression. Previously
proposed joint rank and variable selection approaches assume that different responses are …

Improving the predicting performance of the multiple response regression compared with
separate linear regressions is a challenging question. On the one hand, it is desirable to …

In high dimensional data analysis, regularization methods pursuing sparsity and/or low rank
have received much attention recently. To provide a proper amount of shrinkage, it is typical …

Sparse reduced rank regression achieves dimension reduction and variable selection
simultaneously. In this paper, for a class of nonconvex penalties, we give sufficient …