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A note on rank reduction in sparse multivariate regression
2015
Journal of Statistical Theory and Practice
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 jointly model the multivariate response, the method efficiently constructs a prespecified number of latent variables as some sparse linear combinations of the predictors. Here, we generalize the method to also perform rank reduction, and enable its usage in reduced-rank vector autoregressive (VAR) modeling to perform
doi:10.1080/15598608.2015.1081573
pmid:26997938
pmcid:PMC4797956
fatcat:nspjrdbz3rg47bblolvpgmbuoq