Group-wise sufficient dimension reduction with principal fitted components

DOI10.1007/s00180-015-0611-9zbMath1347.65017OpenAlexW1114252135MaRDI QIDQ311294

Elias Al-Najjar, Sean Martin, Andrew M. Raim, Kofi P. Adragni, Sai K. Popuri

Publication date: 29 September 2016

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-015-0611-9

zbMATH Keywords

predictionclusteringinverse regression

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05)

Related Items (1)

Groupwise sufficient dimension reduction via conditional distance clustering

Uses Software

• R
• cluster (R)
• glasso
• ElemStatLearn
• dr
• ldr

Cites Work

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• Sparse inverse covariance estimation with the graphical lasso
• Fisher lecture: Dimension reduction in regression
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• Exploiting predictor domain information in sufficient dimension reduction
• Least angle regression. (With discussion)
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• On Directional Regression for Dimension Reduction
• Sliced Inverse Regression for Dimension Reduction
• An Adaptive Estimation of Dimension Reduction Space
• Independent screening in high-dimensional exponential family predictors’ space
• Groupwise Dimension Reduction
• Likelihood-Based Sufficient Dimension Reduction
• Groupwise Dimension Reduction via Envelope Method
• Comment
• The elements of statistical learning. Data mining, inference, and prediction

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