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A package on sparse and reduced-rank estimation (rrpack)

  • Package
  • Multivariate regression methodologies including reduced-rank regression (RRR), reduced-rank ridge regression (RRS), robust reduced-rank regression (R4), generalized/mixed-response reduced-rank regression (mRRR), row-sparse reduced-rank regression (SRRR), reduced-rank regression with a sparse singular value decomposition (RSSVD), and sparse and orthogonal factor regression (SOFAR).

Sparse log-contrast regression with functional compositional predictors (compReg)

  • Package
  • References
    • Sun, Z., Xu, W., Cong, X., Chen, K. (2018) Log-contrast regression with functional compositional predictors. arXiv:1808.02403.

Sequential co-sparse factor regression

  • Package
  • References
    • Mishra, A., Dey, D. K., Chen, K. (2017) Sequential co-sparse factor regression. Journal of Computational & Graphical Statistics.

Sparse orthogonal factor regression (sofar)

  • Package (updated implementation is available in the R package rrpack.)
  • References
    • Uematsu, Y., Fan, Y., Chen, K., Lv, J., Lin, W. (2017) Large-scale sparse orthogonal factor regression.

Double single index model

  • Code
  • References
    • Chen, K., Ma, Y. (2017). Analysis of double single index models. Scandinavian Journal of Statistics, 44(1):1–20.

Canonical variate regression

  • Package
  • References
    • Luo, C., Liu, J., Dey, D. K., Chen, K. (2016). Canonical variate regression. Biostatistics, 17(3):468–483.

Reduced rank estimators for multivariate regression

  • Code (updated implementation is available in the R package rrpack.)
  • References
    • Mukherjee, A., Chen, K., Wang N., Zhu, J. (2015). On the degrees of freedom of reduced-rank estimators in multivariate regression. Biometrika, 102(2):457–477, 2015.
    • Chen, K., Dong H. and Chan, K. S. (2013). Reduced rank regression via adaptive nuclear norm penalization. Biometrika, 100 (4), 901-920.

Regularized multivariate regression with a sparse singular value decompostion

  • Code (updated implementation is available in the R package rrpack.)
  • References
    • Chen, K., Chan, K. S. and Stenseth, N. R. (2012). Reduced-rank stochastic regression with a sparse singular value decomposition. Journal of the Royal Statistical Society (Series B), 74 (2), 203-221.

Gibbs posterior for variable selection in high dimensional classification

  • Code
  • Poster
  • References
    • Chen, K., Jiang, W. and Tanner, M. A. (2010). A note on some algorithms for the Gibbs posterior. Statistics & Probability Letters, 80 (15-16), 1234-1241.
    • Jiang, W. and Tanner, M. A. (2008). Gibbs posterior for variable selection in high dimensional classification and data mining. Annals of Statistics. 36, 2207-2231.