hierNet: A Lasso for Hierarchical Interactions. Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect. For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) ”A Lasso for Hierarchical Interactions.” Annals of Statistics. 41(3). 1111-1141.
Keywords for this software
References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Ternès, Nils; Rotolo, Federico; Heinze, Georg; Michiels, Stefan: Identification of biomarker-by-treatment interactions in randomized clinical trials with survival outcomes and high-dimensional spaces (2017)
- He, Yawei; Chen, Zehua: The EBIC and a sequential procedure for feature selection in interactive linear models with high-dimensional data (2016)
- Ustun, Berk; Rudin, Cynthia: Supersparse linear integer models for optimized medical scoring systems (2016)
- Zhao, Junlong; Leng, Chenlei: An analysis of penalized interaction models (2016)
- Bien, Jacob; Simon, Noah; Tibshirani, Robert: Convex hierarchical testing of interactions (2015)
- Bates, Ron A.; Curtis, Peter R.; Maruri-Aguilar, Hugo; Wynn, Henry P.: Optimal design for smooth supersaturated models (2014)
- Jiang, Bo; Liu, Jun S.: Variable selection for general index models via sliced inverse regression (2014)
- Wang, Lu; Shen, Jincheng; Thall, Peter F.: A modified adaptive lasso for identifying interactions in the Cox model with the heredity constraint (2014)
- Abramovich, Felix; Grinshtein, Vadim: Model selection in regression under structural constraints (2013)
- Bien, Jacob; Taylor, Jonathan; Tibshirani, Robert: A lasso for hierarchical interactions (2013)
- Taylor, Jonathan: The geometry of least squares in the 21st century (2013)