Rejoinder: Fisher lecture: Dimension reduction in regression

DOI10.1214/088342307000000078zbMath1246.62149arXiv0708.3781OpenAlexW3101325089MaRDI QIDQ449743

R. Dennis Cook

Publication date: 1 September 2012

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0708.3781

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Linear inference, regression (62J99)

Related Items

Sufficient dimension reduction via principal L\(q\) support vector machine ⋮ Simultaneous dimension reduction and variable selection in modeling high dimensional data ⋮ Revisiting the predictive power of kernel principal components ⋮ Predictive power of principal components for single-index model and sufficient dimension reduction ⋮ A general theory for nonlinear sufficient dimension reduction: formulation and estimation ⋮ Exploiting predictor domain information in sufficient dimension reduction ⋮ On the predictive potential of kernel principal components ⋮ Dimension Reduction via Gaussian Ridge Functions ⋮ Discussion of: Treelets -- an adaptive multi-scale basis for sparse unordered data

Cites Work

• Sampling and Bayes' Inference in Scientific Modelling and Robustness
• Comparison of Prediction Methods When Only a Few Components are Relevant
• Partial Least Squares Estimator for Single-Index Models
• Prediction by Supervised Principal Components
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