rrr.sim1

n, p, q

nrank

error variance. If specfied, then s2n has no effect

sigma

correlation parameter in the generation of predictors

rho_X

correlation parameter in the generation of random errors

rho_E

Similar to the the RSSVD simulation model in Chen, Chan, Stenseth (2012),
JRSSB.

Multivariate regression methodologies including
classical reduced-rank regression (RRR)
studied by Anderson (1951) <doi:10.1214/aoms/1177729580> and
Reinsel and Velu (1998) <doi:10.1007/978-1-4757-2853-8>,
reduced-rank regression via adaptive nuclear norm penalization
proposed by Chen et al. (2013) <doi:10.1093/biomet/ast036> and
Mukherjee et al. (2015) <doi:10.1093/biomet/asx080>,
robust reduced-rank regression (R4) proposed by
She and Chen (2017) <doi:10.1093/biomet/asx032>,
generalized/mixed-response reduced-rank regression (mRRR) proposed by
Luo et al. (2018) <doi:10.1016/j.jmva.2018.04.011>,
row-sparse reduced-rank regression (SRRR) proposed by
Chen and Huang (2012) <doi:10.1080/01621459.2012.734178>,
reduced-rank regression with a sparse singular value decomposition (RSSVD)
proposed by Chen et al. (2012) <doi:10.1111/j.1467-9868.2011.01002.x>
and sparse and orthogonal factor regression (SOFAR) proposed by
Uematsu et al. (2019) <doi:10.1109/TIT.2019.2909889>.

rrr.sim1: Simulation model 1

Description

Usage

Arguments

Value

References