Font Size:
Bootstrap group penalty for high-dimensional regression models
Last modified: 2017-05-22
Abstract
The paper presents a new penalization procedure for variable selection in
regression models.We propose the Bootstrap Group Penalty (BGP) that extends the
bootstrap version of the LASSO method by taking into account the grouping structure
which may be present or introduced in a model. Based on a simulation study we
demonstrate that the new procedure outperforms some existing group penalization
methods in terms of both prediction accuracy and variable selection quality.
regression models.We propose the Bootstrap Group Penalty (BGP) that extends the
bootstrap version of the LASSO method by taking into account the grouping structure
which may be present or introduced in a model. Based on a simulation study we
demonstrate that the new procedure outperforms some existing group penalization
methods in terms of both prediction accuracy and variable selection quality.