Estimating a sparse reduction for general regression in high dimensions

DOI10.1007/s11222-016-9714-6zbMath1384.62141OpenAlexW2536525504MaRDI QIDQ1702278

Mengjie Chen, Tao Wang, Hongyu Zhao, Li Xing Zhu

Publication date: 28 February 2018

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-016-9714-6

zbMATH Keywords

sparsityinverse modelingmodel-free dimension reduction

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (5)

Unnamed Item ⋮ Double-slicing assisted sufficient dimension reduction for high-dimensional censored data ⋮ Dimension reduction for block-missing data based on sparse sliced inverse regression ⋮ Fourier transform sparse inverse regression estimators for sufficient variable selection ⋮ Sparse Minimum Discrepancy Approach to Sufficient Dimension Reduction with Simultaneous Variable Selection in Ultrahigh Dimension

Uses Software

• RSIR

Cites Work

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• Comment
• Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors

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