PMA

R package PMA: Penalized Multivariate Analysis. Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in the following papers: (1) Witten, Tibshirani and Hastie (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10(3):515-534. (2) Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data. Statistical Applications in Genetics and Molecular Biology 8(1): Article 28.


References in zbMATH (referenced in 114 articles )

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  1. Langworthy, Benjamin W.; Stephens, Rebecca L.; Gilmore, John H.; Fine, Jason P.: Canonical correlation analysis for elliptical copulas (2021)
  2. Cai, Jia; Huo, Junyi: Sparse generalized canonical correlation analysis via linearized Bregman method (2020)
  3. Chi, Eric C.; Gaines, Brian J.; Sun, Will Wei; Zhou, Hua; Yang, Jian: Provable convex co-clustering of tensors (2020)
  4. Erichson, N. Benjamin; Zheng, Peng; Manohar, Krithika; Brunton, Steven L.; Kutz, J. Nathan; Aravkin, Aleksandr Y.: Sparse principal component analysis via variable projection (2020)
  5. Malec, Lukáš; Janovský, Vladimír: Connecting the multivariate partial least squares with canonical analysis: a path-following approach (2020)
  6. Ma, Zhuang; Li, Xiaodong: Subspace perspective on canonical correlation analysis: dimension reduction and minimax rates (2020)
  7. Mukhopadhyay, Minerva; Dunson, David B.: Targeted random projection for prediction from high-dimensional features (2020)
  8. Pan, Yuqing; Mai, Qing: Efficient computation for differential network analysis with applications to quadratic discriminant analysis (2020)
  9. Wang, Yiju; Dong, Manman; Xu, Yi: A sparse rank-1 approximation algorithm for high-order tensors (2020)
  10. Xia, Yin; Li, Lexin; Lockhart, Samuel N.; Jagust, William J.: Simultaneous covariance inference for multimodal integrative analysis (2020)
  11. Xiu, Xianchao; Yang, Ying; Kong, Lingchen; Liu, Wanquan: tSSNALM: a fast two-stage semi-smooth Newton augmented Lagrangian method for sparse CCA (2020)
  12. Zhang, Fan; Miecznikowski, Jeffrey C.; Tritchler, David L.: Identification of supervised and sparse functional genomic pathways (2020)
  13. Zhang, Fan; Wang, Hao; Wang, Jiashan; Yang, Kai: Inexact primal-dual gradient projection methods for nonlinear optimization on convex set (2020)
  14. Zhao, Yi; Lindquist, Martin A.; Caffo, Brian S.: Sparse principal component based high-dimensional mediation analysis (2020)
  15. Zhou, Yicheng; Lu, Zhenzhou; Hu, Jinghan; Hu, Yingshi: Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square (2020)
  16. Berk, Lauren; Bertsimas, Dimitris: Certifiably optimal sparse principal component analysis (2019)
  17. Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon: Lasso meets horseshoe: a survey (2019)
  18. Chakrabarti, Arnab; Sen, Rituparna: Some statistical problems with high dimensional financial data (2019)
  19. De Micheaux, Pierre Lafaye; Liquet, Benoît; Sutton, Matthew: PLS for Big Data: a unified parallel algorithm for regularised group PLS (2019)
  20. Fan, Zhou; Montanari, Andrea: The spectral norm of random inner-product kernel matrices (2019)

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