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 76 articles )

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  1. Fan, Zhou; Montanari, Andrea: The spectral norm of random inner-product kernel matrices (2019)
  2. She, Yiyuan; Tran, Hoang: On cross-validation for sparse reduced rank regression (2019)
  3. Cai, T. Tony; Zhang, Anru: Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics (2018)
  4. Chiquet, Julien; Mariadassou, Mahendra; Robin, Stéphane: Variational inference for probabilistic Poisson PCA (2018)
  5. Fang, Kuangnan; Fan, Xinyan; Zhang, Qingzhao; Ma, Shuangge: Integrative sparse principal component analysis (2018)
  6. Feng, Qing; Jiang, Meilei; Hannig, Jan; Marron, J. S.: Angle-based joint and individual variation explained (2018)
  7. Han, Fang; Liu, Han: ECA: high-dimensional elliptical component analysis in non-Gaussian distributions (2018)
  8. Jung, Sungkyu: Continuum directions for supervised dimension reduction (2018)
  9. Kawano, Shuichi; Fujisawa, Hironori; Takada, Toyoyuki; Shiroishi, Toshihiko: Sparse principal component regression for generalized linear models (2018)
  10. Li, Gen; Gaynanova, Irina: A general framework for association analysis of heterogeneous data (2018)
  11. Martín-Fernández, J. A.; Pawlowsky-Glahn, V.; Egozcue, J. J.; Tolosona-Delgado, R.: Advances in principal balances for compositional data (2018)
  12. Boulesteix, Anne-Laure; De Bin, Riccardo; Jiang, Xiaoyu; Fuchs, Mathias: IPF-LASSO: integrative (L_1)-penalized regression with penalty factors for prediction based on multi-omics data (2017)
  13. Hou, Thomas Y.; Li, Qin; Zhang, Pengchuan: A sparse decomposition of low rank symmetric positive semidefinite matrices (2017)
  14. Hou, Thomas Y.; Zhang, Pengchuan: Sparse operator compression of higher-order elliptic operators with rough coefficients (2017)
  15. Zhu, Hong; Zhang, Xiaowei; Chu, Delin; Liao, Li-Zhi: Nonconvex and nonsmooth optimization with generalized orthogonality constraints: an approximate augmented Lagrangian method (2017)
  16. Adachi, Kohei; Trendafilov, Nickolay T.: Sparse principal component analysis subject to prespecified cardinality of loadings (2016)
  17. Alfons, Andreas; Croux, Christophe; Gelper, Sarah: Robust groupwise least angle regression (2016)
  18. Ames, Brendan P. W.; Hong, Mingyi: Alternating direction method of multipliers for penalized zero-variance discriminant analysis (2016)
  19. Bai, Jushan; Liao, Yuan: Efficient estimation of approximate factor models via penalized maximum likelihood (2016)
  20. Beck, Amir; Vaisbourd, Yakov: The sparse principal component analysis problem: optimality conditions and algorithms (2016)

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