Sufficient dimension reduction and prediction through cumulative slicing PFC

DOI10.1080/00949655.2018.1425688OpenAlexW2791654433MaRDI QIDQ4960600

Xiang-Jie Li, Jing-Xiao Zhang, Xinyi Xu

Publication date: 23 April 2020

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

Full work available at URL: https://doi.org/10.1080/00949655.2018.1425688

zbMATH Keywords

sufficient dimension reductioncumulative slicing basisprinciple fitted component model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12)

Cites Work

• Prediction in abundant high-dimensional linear regression
• Fisher lecture: Dimension reduction in regression
• Principal fitted components for dimension reduction in regression
• Successive direction extraction for estimating the central subspace in a multiple-index regres\-sion
• Testing predictor contributions in sufficient dimension reduction.
• Theoretical properties of Cook's PFC dimension reduction algorithm for linear regression
• Contour regression: a general approach to dimension reduction
• Sufficient dimension reduction and prediction in regression
• On Directional Regression for Dimension Reduction
• Determining the Dimension in Sliced Inverse Regression and Related Methods
• Sliced Inverse Regression for Dimension Reduction
• Graphics for Regressions With a Binary Response
• Dimension Reduction in Regressions Through Cumulative Slicing Estimation
• Likelihood-Based Sufficient Dimension Reduction
• Sufficient Dimension Reduction via Inverse Regression
• Comment

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