sJIVE: Supervised joint and individual variation explained
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DOI: 10.1016/j.csda.2022.107547
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References listed on IDEAS
- Gen Li & Sungkyu Jung, 2017. "Incorporating covariates into integrated factor analysis of multi‐view data," Biometrics, The International Biometric Society, vol. 73(4), pages 1433-1442, December.
- Irina Gaynanova & Gen Li, 2019. "Structural learning and integrative decomposition of multi‐view data," Biometrics, The International Biometric Society, vol. 75(4), pages 1121-1132, December.
- Witten Daniela M & Tibshirani Robert J., 2009. "Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-29, June.
- Bair, Eric & Hastie, Trevor & Paul, Debashis & Tibshirani, Robert, 2006. "Prediction by Supervised Principal Components," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 119-137, March.
- Feng, Qing & Jiang, Meilei & Hannig, Jan & Marron, J.S., 2018. "Angle-based joint and individual variation explained," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 241-265.
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Cited by:
- Kim, Jonathan & Sandri, Brian J. & Rao, Raghavendra B. & Lock, Eric F., 2023. "Bayesian predictive modeling of multi-source multi-way data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).
- Samorodnitsky, Sarah & Wendt, Chris H. & Lock, Eric F., 2024. "Bayesian simultaneous factorization and prediction using multi-omic data," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).
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Keywords
Data integration; Dimension reduction; Genomic data; High-dimensional prediction; Multi-source data; Multi-view learning;All these keywords.
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