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 95 articles )

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  1. Ma, Zhuang; Li, Xiaodong: Subspace perspective on canonical correlation analysis: dimension reduction and minimax rates (2020)
  2. Pan, Yuqing; Mai, Qing: Efficient computation for differential network analysis with applications to quadratic discriminant analysis (2020)
  3. Zhao, Yi; Lindquist, Martin A.; Caffo, Brian S.: Sparse principal component based high-dimensional mediation analysis (2020)
  4. Berk, Lauren; Bertsimas, Dimitris: Certifiably optimal sparse principal component analysis (2019)
  5. Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon: Lasso meets horseshoe: a survey (2019)
  6. Chakrabarti, Arnab; Sen, Rituparna: Some statistical problems with high dimensional financial data (2019)
  7. De Micheaux, Pierre Lafaye; Liquet, Benoît; Sutton, Matthew: PLS for Big Data: a unified parallel algorithm for regularised group PLS (2019)
  8. Fan, Zhou; Montanari, Andrea: The spectral norm of random inner-product kernel matrices (2019)
  9. Fukuyama, Julia: Adaptive gPCA: a method for structured dimensionality reduction with applications to microbiome data (2019)
  10. Galeano, Pedro; Peña, Daniel: Data science, big data and statistics (2019)
  11. 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)
  12. Keys, Kevin L.; Zhou, Hua; Lange, Kenneth: Proximal distance algorithms: theory and practice (2019)
  13. Sabnis, Gautam; Pati, Debdeep; Bhattacharya, Anirban: Compressed covariance estimation with automated dimension learning (2019)
  14. She, Yiyuan; Tran, Hoang: On cross-validation for sparse reduced rank regression (2019)
  15. Sutton, Matthew; Mengersen, Kerrie; Liquet, Benoit: [HDDA] sparse subspace constrained partial least squares (2019)
  16. Cai, T. Tony; Zhang, Anru: Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics (2018)
  17. Chiquet, Julien; Mariadassou, Mahendra; Robin, Stéphane: Variational inference for probabilistic Poisson PCA (2018)
  18. Fang, Kuangnan; Fan, Xinyan; Zhang, Qingzhao; Ma, Shuangge: Integrative sparse principal component analysis (2018)
  19. Feng, Qing; Jiang, Meilei; Hannig, Jan; Marron, J. S.: Angle-based joint and individual variation explained (2018)
  20. Han, Fang; Liu, Han: ECA: high-dimensional elliptical component analysis in non-Gaussian distributions (2018)

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