Sufficient dimension folding via tensor inverse regression

DOI10.1080/00949655.2020.1730372OpenAlexW3008412275MaRDI QIDQ5107782

Jing-Xiao Zhang, Xiang-Jie Li

Publication date: 28 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2020.1730372

zbMATH Keywords

tensor-valued datainverse regression estimationsufficient dimension folding

Mathematics Subject Classification ID

Multivariate analysis (62H99) Estimation in multivariate analysis (62H12) Differential geometric aspects in vector and tensor analysis (53A45)

Uses Software

• ARC

Cites Work

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• Tensor Decompositions and Applications
• Tensor sliced inverse regression
• Coordinate-independent sparse sufficient dimension reduction and variable selection
• Regression analysis under link violation
• Testing predictor contributions in sufficient dimension reduction.
• Sufficient dimension reduction in regressions through cumulative Hessian directions
• On dimension folding of matrix- or array-valued statistical objects
• Contour regression: a general approach to dimension reduction
• On a Projective Resampling Method for Dimension Reduction With Multivariate Responses
• On Directional Regression for Dimension Reduction
• Principal Hessian Directions Revisited
• Sliced Inverse Regression for Dimension Reduction
• A Semiparametric Approach to Dimension Reduction
• A Review on Dimension Reduction
• Dimension Reduction in Regressions Through Cumulative Slicing Estimation
• Dimension folding PCA and PFC for matrix-valued predictors
• Sufficient Dimension Reduction via Inverse Regression
• Comment
• Generalized low rank approximations of matrices

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