Likelihood-Based Dimension Folding on Tensor Data

DOI10.5705/ss.202020.0040OpenAlexW4200042377WikidataQ108863862 ScholiaQ108863862MaRDI QIDQ5040484

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Publication date: 14 October 2022

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

Full work available at URL: https://doi.org/10.5705/ss.202020.0040

zbMATH Keywords

tensorsufficient dimension reductionquadratic discriminant analysisdimension folding

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Inference for the Dimension of a Regression Relationship Using Pseudo-Covariates

Uses Software

• SupCP
• ROPTLIB

Cites Work

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• Supervised multiway factorization
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• Sparse Quadratic Discriminant Analysis For High Dimensional Data
• On Directional Regression for Dimension Reduction
• Asymptotic Theory of Overparameterized Structural Models
• Sliced Inverse Regression for Dimension Reduction
• Sufficient dimension folding in regression via distance covariance for matrix‐valued predictors
• Common reducing subspace model and network alternation analysis
• Likelihood-Based Sufficient Dimension Reduction
• Sufficient dimension folding for a functional of conditional distribution of matrix- or array-valued objects
• Tensor Regression with Applications in Neuroimaging Data Analysis
• Dimension folding PCA and PFC for matrix-valued predictors
• Comment

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