D-CCA: A Decomposition-Based Canonical Correlation Analysis for High-Dimensional Datasets

DOI10.1080/01621459.2018.1543599zbMath1437.62211OpenAlexW2905533385WikidataQ104459534 ScholiaQ104459534MaRDI QIDQ3304854

No author found.

Publication date: 3 August 2020

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7731964

zbMATH Keywords

soft thresholdingapproximate factor modelcommon structurecanonical variabledistinctive structure

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (9)

CDPA: common and distinctive pattern analysis between high-dimensional datasets ⋮ Double-Matched Matrix Decomposition for Multi-View Data ⋮ Decomposition of Variation of Mixed Variables by a Latent Mixed Gaussian Copula Model ⋮ Percolate: an exponential family JIVE model to design DNA-based predictors of drug response ⋮ Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis ⋮ Generalized Liquid Association Analysis for Multimodal Data Integration ⋮ Integrative Factor Regression and Its Inference for Multimodal Data Analysis ⋮ Determining the number of canonical correlation pairs for high-dimensional vectors ⋮ Eigenvector-based sparse canonical correlation analysis: fast computation for estimation of multiple canonical vectors

Uses Software

• JIVE
• OnPLS

Cites Work

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• Measuring and testing dependence by correlation of distances
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• Eigenvalue Ratio Test for the Number of Factors
• Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
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• Numerical Methods for Computing Angles Between Linear Subspaces
• Sparse CCA via Precision Adjusted Iterative Thresholding
• Inferential Theory for Factor Models of Large Dimensions
• Determining the Number of Factors in Approximate Factor Models
• Canonical analysis of several sets of variables
• Large Covariance Estimation by Thresholding Principal Orthogonal Complements
• RELATIONS BETWEEN TWO SETS OF VARIATES

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