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).
- References
- Reinsel, G. C., Velu, R. P., and Chen, K. (2022) Multivariate Reduced-Rank Regression: Theory, Methods and Applications, 2nd Edition. Springer.
- Reinsel, G. C., Velu, R. P., and Chen, K. (2022) Multivariate Reduced-Rank Regression: Theory, Methods and Applications, 2nd Edition. Springer.
Tree-guided rare feature selection and logic aggregation (TSLA)
- Package
- References
- Chen, J., Aseltine, R., Wang, F., and Chen, K. (2024) Tree-guided rare feature selection and logic aggregation with electronic health records data. Journal of the American Statistical Association, 119 (547):1765–1777.
- Chen, J., Aseltine, R., Wang, F., and Chen, K. (2024) Tree-guided rare feature selection and logic aggregation with electronic health records data. Journal of the American Statistical Association, 119 (547):1765–1777.
Integrative survival modeling (intsurv)
- Package
- References
- Wang, W., Aseltine, R. H., Chen, K., & Yan, J. (2020). Integrative Survival Analysis with Uncertain Event Times in Application to A Suicide Risk Study. Annals of Applied Statistics, 14(1), 51-73.
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.
Log-contrast regression with functional compositional predictors (compReg)
- Package
- References
- Sun, Z., Xu, W., Cong, X., Li, G., and Chen, K. (2020) Log-contrast regression with functional compositional predictors: Linking preterm infant’s gut microbiome trajectories to neurobehavioral outcome. Annals of Applied Statistics, 14(3):1535–1556. (2020 John van Ryzin Award and ENAR Distinguished Student Paper Award).
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.