Dimension folding PCA and PFC for matrix-valued predictors

DOI10.5705/ss.2012.138zbMath1416.62325OpenAlexW2053751277MaRDI QIDQ5413267

Shanshan Ding, R. Dennis Cook

Publication date: 29 April 2014

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.5705/ss.2012.138

zbMATH Keywords

sufficient dimension reductioninverse regressioncentral subspacematrix normal distributioncentral dimension folding subspace

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25)

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Likelihood-Based Dimension Folding on Tensor Data ⋮ Model-Free Variable Selection With Matrix-Valued Predictors ⋮ Ensemble sufficient dimension folding methods for analyzing matrix-valued data ⋮ Independent component analysis for tensor-valued data ⋮ Sufficient dimension folding via tensor inverse regression ⋮ Joint Bayesian longitudinal models for mixed outcome types and associated model selection techniques ⋮ Tensor sliced inverse regression ⋮ L2RM: Low-Rank Linear Regression Models for High-Dimensional Matrix Responses ⋮ JADE for Tensor-Valued Observations ⋮ Sliced inverse regression for integrative multi-omics data analysis ⋮ Sparse Minimum Discrepancy Approach to Sufficient Dimension Reduction with Simultaneous Variable Selection in Ultrahigh Dimension ⋮ Unnamed Item

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