Revisiting the predictive power of kernel principal components

DOI10.1016/j.spl.2020.109019zbMath1461.62088OpenAlexW3114017412MaRDI QIDQ2658000

Ben Jones, Andreas Artemiou

Publication date: 18 March 2021

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2020.109019

zbMATH Keywords

dimension reductionHilbert spacesprincipal componentsconditional independencemodel-free regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Factor analysis and principal components; correspondence analysis (62H25)

Cites Work

• Unnamed Item
• Predictive power of principal components for single-index model and sufficient dimension reduction
• Rejoinder: Fisher lecture: Dimension reduction in regression
• Dimension reduction strategies for analyzing global gene expression data with a response
• On the predictive potential of kernel principal components
• On principal components regression with Hilbertian predictors
• Principal component regression revisited
• Comment: Fisher lecture: Dimension reduction in regression

This page was built for publication: Revisiting the predictive power of kernel principal components