ENVELOPE MODELS FOR PARSIMONIOUS AND EFFICIENT MULTIVARIATE LINEAR REGRESSION
By R. Dennis Cook, Bing Li, Francesca Chiaromonte. 2010
198 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/436c91247241e9c486e79b796231ff39dc64e49b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- R. Dennis Cook
- Kofi P. Adragni
- Liliana Forzani
- Zhihua Su
- Andrew M. Raim
- Xiangrong Yin
- Bing Li
- Praneeth Vepakomma
Citations in the corpus
- Sparse envelope model: efficient estimation and response variable selection in multivariate linear regression (2016, unknown): “The envelope model (Cook et al., 2010) makes use of the stochastic relationships among the elements of Y , and identifies a part of the response that is immaterial to changes in X .”
- Envelopes and reduced-rank regression (2015, methodology): “…maximum likelihood estimators.269 We focus attention on the comparison between β̂RE and β̂RR because (1) comparisons between270 β̂Env and β̂OLS can be found in Cook et al. (2010); and (2) the advantage of β̂RE over β̂Env is271 similar to the advantage of β̂RR over β̂OLS, which is due to the…”
- Partial least squares prediction in high-dimensional regression (2019, methodology): “Cook, Helland and Su [12] established a population connection between PLS regression and envelopes [14] in the context of multivariate linear regression, provided the first firm PLS model and showed that envelope estimation leads to root-n consistent estimators whose performance dominates that of PL”
- Inner envelopes: Efficient estimation in multivariate linear regression (2012, background): “As β = 1ηT1 + 0 BηT2, we follow Lemma 4.2 in Cook et al. (2010) and write L = L1L2, where L1 = {det( 1)}−n/2 × etr{−2−1(U 1 − Fη1) −11 (U 1 − Fη1)T}, L2 = {det( 0)}−n/2 × etr{−2−1(U 0 − Fη2 BT) −10 (U 0 − Fη2 BT)T}.”
- ldr: An R Software Package for Likelihood-Based Su?cient Dimension Reduction (2014, methodology): “…the predictor vector X given the response Y , and include covariance reduction (CORE, Cook and Forzani 2008a), likelihood acquired directions (LAD, Cook and Forzani 2009), principal fitted components (PFC, Cook 2007; Cook and Forzani 2008b), and the envelope model (Cook, Li, and Chiaromonte 2010).”