Sparse sufficient dimension reduction with heteroscedasticity

DOI10.1142/S0219691321500375zbMath1482.62059OpenAlexW3183302431MaRDI QIDQ5063222

Haoyang Cheng, Wenquan Cui

Publication date: 17 March 2022

Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0219691321500375

zbMATH Keywords

heteroscedasticityLassolarge \(p\) small \(n\)principal projectionsparse sufficient dimension reductionprincipal quantile regression

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07)

Cites Work

• A constructive approach to the estimation of dimension reduction directions
• Sparse Sliced Inverse Regression Via Lasso
• An adaptive composite quantile approach to dimension reduction
• Principal quantile regression for sufficient dimension reduction with heteroscedasticity
• On consistency and sparsity for sliced inverse regression in high dimensions
• Asymptotics for kernel estimate of sliced inverse regression
• On Directional Regression for Dimension Reduction
• On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
• An Adaptive Estimation of Dimension Reduction Space
• A Semiparametric Approach to Dimension Reduction
• Comment

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