Using DAGs to identify the sufficient dimension reduction in the principal fitted components model

DOI10.1016/j.spl.2018.08.008zbMath1414.62221OpenAlexW2889265001WikidataQ129336964 ScholiaQ129336964MaRDI QIDQ1726806

María Eugenia Szretter Noste

Publication date: 20 February 2019

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

Full work available at URL: https://doi.org/10.1016/j.spl.2018.08.008

zbMATH Keywords

conditional independencesufficient dimension reductiondirected acyclic graphsprincipal fitted components model

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of graph theory (05C90)

Related Items (2)

A robust proposal of estimation for the sufficient dimension reduction problem ⋮ A slice of multivariate dimension reduction

Cites Work

• Fisher lecture: Dimension reduction in regression
• Principal support vector machines for linear and nonlinear sufficient dimension reduction
• Principal fitted components for dimension reduction in regression
• The sliced inverse regression algorithm as a maximum likelihood procedure
• Model-based SIR for dimension reduction
• On Directional Regression for Dimension Reduction
• Sliced Inverse Regression for Dimension Reduction
• On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
• The multivariate skew-normal distribution
• Sufficient Reductions in Regressions With Elliptically Contoured Inverse Predictors
• Sufficient Dimension Reduction via Inverse Regression
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