SIS

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

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 4 articles , 1 standard article )

Showing results 1 to 4 of 4.
Sorted by year (citations)

  1. Diego Saldana; Yang Feng: SIS: An R Package for Sure Independence Screening in Ultrahigh-Dimensional Statistical Models (2018) not zbMATH
  2. Boukouvala, Fani; Floudas, Christodoulos A.: ARGONAUT: algorithms for global optimization of constrained grey-box computational problems (2017)
  3. Razieh Nabi Abdolyousefi, Xiaogang Su: coxphMIC: An R Package for Sparse Estimation of Cox Proportional Hazards Models (2016) arXiv
  4. Schifano, Elizabeth D.; Strawderman, Robert L.; Wells, Martin T.: Majorization-minimization algorithms for nonsmoothly penalized objective functions (2010)