SiER: Signal Extraction Approach for Sparse Multivariate Response Regression

Methods for regression with high-dimensional predictors and univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.

Getting started

Package details

AuthorRuiyan, Xin Qi
MaintainerRuiyan Luo <[email protected]>
LicenseGPL-2
Version0.1.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("SiER")

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SiER documentation built on Sept. 19, 2017, 9:02 a.m.