Envelope-based sparse partial least squares

DOI10.1214/18-AOS1796zbMath1439.62174OpenAlexW3007540310MaRDI QIDQ2176612

Zhihua Su, Guang-Yu Zhu

Publication date: 5 May 2020

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aos/1581930130

zbMATH Keywords

partial least squaresGrassmann manifoldsufficient dimension reductionenvelope model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Statistics on manifolds (62R30) Linear regression; mixed models (62J05) Generalized linear models (logistic models) (62J12) Sufficient statistics and fields (62B05)

Related Items (8)

A Bayesian Approach to Envelope Quantile Regression ⋮ Efficient simultaneous partial envelope model in multivariate linear regression ⋮ Unnamed Item ⋮ Scaled Partial Envelope Model in Multivariate Linear Regression ⋮ Envelopes and principal component regression ⋮ Envelope-based sparse reduced-rank regression for multivariate linear model ⋮ Efficient estimation of reduced-rank partial envelope model in multivariate linear regression ⋮ A slice of multivariate dimension reduction

Uses Software

• UCI-ml
• mixOmics

Cites Work

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• Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection
• Envelopes and Partial Least Squares Regression
• Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection
• Partial Correlation Estimation by Joint Sparse Regression Models
• Foundations for Envelope Models and Methods
• A Convex Pseudolikelihood Framework for High Dimensional Partial Correlation Estimation with Convergence Guarantees
• Sparse envelope model: efficient estimation and response variable selection in multivariate linear regression
• Sparse precision matrix estimation via lasso penalized D-trace loss
• Efficiency loss and the linearity condition in dimension reduction

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