R package SIS: SIS: Sure Independence Screening. Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) and all of its variants in generalized linear models and the Cox proportional hazards model.
Keywords for this software
References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
- Pun, Chi Seng; Hadimaja, Matthew Zakharia: A self-calibrated direct approach to precision matrix estimation and linear discriminant analysis in high dimensions (2021)
- Zhao, Bangxin; Liu, Xin; He, Wenqing; Yi, Grace Y.: Dynamic tilted current correlation for high dimensional variable screening (2021)
- Baranowski, Rafal; Chen, Yining; Fryzlewicz, Piotr: Ranking-based variable selection for high-dimensional data (2020)
- Diego Saldana; Yang Feng: SIS: An R Package for Sure Independence Screening in Ultrahigh-Dimensional Statistical Models (2018) not zbMATH
- Boukouvala, Fani; Floudas, Christodoulos A.: ARGONAUT: algorithms for global optimization of constrained grey-box computational problems (2017)
- Razieh Nabi Abdolyousefi, Xiaogang Su: coxphMIC: An R Package for Sparse Estimation of Cox Proportional Hazards Models (2016) arXiv
- Zhong, Wei; Zhu, Liping: An iterative approach to distance correlation-based sure independence screening (2015)
- Schifano, Elizabeth D.; Strawderman, Robert L.; Wells, Martin T.: Majorization-minimization algorithms for nonsmoothly penalized objective functions (2010)