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 102 articles )

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  1. Cai, Jia; Huo, Junyi: Sparse generalized canonical correlation analysis via linearized Bregman method (2020)
  2. Erichson, N. Benjamin; Zheng, Peng; Manohar, Krithika; Brunton, Steven L.; Kutz, J. Nathan; Aravkin, Aleksandr Y.: Sparse principal component analysis via variable projection (2020)
  3. Ma, Zhuang; Li, Xiaodong: Subspace perspective on canonical correlation analysis: dimension reduction and minimax rates (2020)
  4. Pan, Yuqing; Mai, Qing: Efficient computation for differential network analysis with applications to quadratic discriminant analysis (2020)
  5. Wang, Yiju; Dong, Manman; Xu, Yi: A sparse rank-1 approximation algorithm for high-order tensors (2020)
  6. Xiu, Xianchao; Yang, Ying; Kong, Lingchen; Liu, Wanquan: tSSNALM: a fast two-stage semi-smooth Newton augmented Lagrangian method for sparse CCA (2020)
  7. Zhao, Yi; Lindquist, Martin A.; Caffo, Brian S.: Sparse principal component based high-dimensional mediation analysis (2020)
  8. 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)
  9. Berk, Lauren; Bertsimas, Dimitris: Certifiably optimal sparse principal component analysis (2019)
  10. Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon: Lasso meets horseshoe: a survey (2019)
  11. Chakrabarti, Arnab; Sen, Rituparna: Some statistical problems with high dimensional financial data (2019)
  12. De Micheaux, Pierre Lafaye; Liquet, Benoît; Sutton, Matthew: PLS for Big Data: a unified parallel algorithm for regularised group PLS (2019)
  13. Fan, Zhou; Montanari, Andrea: The spectral norm of random inner-product kernel matrices (2019)
  14. Fukuyama, Julia: Adaptive gPCA: a method for structured dimensionality reduction with applications to microbiome data (2019)
  15. Galeano, Pedro; Peña, Daniel: Data science, big data and statistics (2019)
  16. Huang, Lei; Bai, Jiawei; Ivanescu, Andrada; Harris, Tamara; Maurer, Mathew; Green, Philip; Zipunnikov, Vadim: Multilevel matrix-variate analysis and its application to accelerometry-measured physical activity in clinical populations (2019)
  17. Keys, Kevin L.; Zhou, Hua; Lange, Kenneth: Proximal distance algorithms: theory and practice (2019)
  18. Sabnis, Gautam; Pati, Debdeep; Bhattacharya, Anirban: Compressed covariance estimation with automated dimension learning (2019)
  19. She, Yiyuan; Tran, Hoang: On cross-validation for sparse reduced rank regression (2019)
  20. Sutton, Matthew; Mengersen, Kerrie; Liquet, Benoit: [HDDA] sparse subspace constrained partial least squares (2019)

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