Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
SIS: An R Package for Sure Independence Screening in Ultrahigh-Dimensional Statistical Models | Saldana | Journal of Statistical Software
Authors: Diego Franco Saldana, Yang Feng
Title: SIS: An R Package for Sure Independence Screening in Ultrahigh-Dimensional Statistical Models
Abstract: We revisit sure independence screening procedures for variable selection in generalized linear models and the Cox proportional hazards model. Through the publicly available R package SIS, we provide a unified environment to carry out variable selection using iterative sure independence screening (ISIS) and all of its variants. For the regularization steps in the ISIS recruiting process, available penalties include the LASSO, SCAD, and MCP while the implemented variants for the screening steps are sample splitting, data-driven thresholding, and combinations thereof. Performance of these feature selection techniques is investigated by means of real and simulated data sets, where we find considerable improvements in terms of model selection and computational time between our algorithms and traditional penalized pseudo-likelihood methods applied directly to the full set of covariates.

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Paper: SIS: An R Package for Sure Independence Screening in Ultrahigh-Dimensional Statistical Models     Download PDF (Downloads: 578)
Supplements:
SIS_0.8-6.tar.gz: R source package Download (Downloads: 46; 2MB)
v83i02-replication.zip: Replication materials Download (Downloads: 58; 5KB)

DOI: 10.18637/jss.v083.i02

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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.