Data-driven slicing for dimension reduction in regressions: A likelihood-ratio approach

DOI10.1007/S11425-022-2088-XMaRDI QIDQ6492450

Li Xing Zhu, Tao Wang, Pei-Rong Xu

Publication date: 25 April 2024

Published in: Science China. Mathematics (Search for Journal in Brave)

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) General nonlinear regression (62J02)

Cites Work

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• A minimum discrepancy approach to multivariate dimension reduction via \(k\)-means inverse regression
• Asymptotics for sliced average variance estimation
• On hybrid methods of inverse regression-based algorithms
• Sliced Inverse Regression for Dimension Reduction
• Using the Bootstrap to Select One of a New Class of Dimension Reduction Methods
• Theory & Methods: Special Invited Paper: Dimension Reduction and Visualization in Discriminant Analysis (with discussion)
• Fused Estimators of the Central Subspace in Sufficient Dimension Reduction
• Dimension reduction via adaptive slicing
• Dimension Reduction in Regressions Through Cumulative Slicing Estimation
• Likelihood-Based Sufficient Dimension Reduction
• RELATIONS BETWEEN TWO SETS OF VARIATES
• Comment

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