Pruning a sufficient dimension reduction with ap-value guided hard-thresholding

DOI10.1080/02331888.2015.1050019zbMath1360.62298OpenAlexW1486520253MaRDI QIDQ5739663

Kofi P. Adragni, Mingyu Xi

Publication date: 19 July 2016

Published in: Unnamed Author (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2015.1050019

zbMATH Keywords

dimension reductionvariable selectionsparsityprincipal fitted component

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Factor analysis and principal components; correspondence analysis (62H25) Ridge regression; shrinkage estimators (Lasso) (62J07) Point estimation (62F10)

Uses Software

• ElemStatLearn

Cites Work

• Fisher lecture: Dimension reduction in regression
• Coordinate-independent sparse sufficient dimension reduction and variable selection
• Principal fitted components for dimension reduction in regression
• Testing predictor contributions in sufficient dimension reduction.
• Least angle regression. (With discussion)
• Variable selection for the single-index model
• Sufficient dimension reduction and prediction in regression
• On Directional Regression for Dimension Reduction
• Regularization and Variable Selection Via the Elastic Net
• Sparse sufficient dimension reduction
• Prediction by Supervised Principal Components
• The elements of statistical learning. Data mining, inference, and prediction

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