Weighted generalized ridge regression offers unique advantages in correlated high-dimensional problems. Such estimators can be efficiently computed using Bayesian spike and slab models and are effective for prediction. For sparse variable selection, a generalization of the elastic net can be used in tandem with these Bayesian estimates. In this article, we describe the R-software package spikeslab for implementing this new spike and slab prediction and variable selection methodology.
HighPerformanceComputing, MachineLearning
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For attribution, please cite this work as
Ishwaran, et al., "The R Journal: spikeslab: Prediction and Variable Selection Using Spike and Slab Regression", The R Journal, 2010
BibTeX citation
@article{RJ-2010-018,
author = {Ishwaran, Hemant and Kogalur, Udaya B. and Rao, J. Sunil},
title = {The R Journal: spikeslab: Prediction and Variable Selection Using Spike and Slab Regression},
journal = {The R Journal},
year = {2010},
note = {https://doi.org/10.32614/RJ-2010-018},
doi = {10.32614/RJ-2010-018},
volume = {2},
issue = {2},
issn = {2073-4859},
pages = {68-73}
}