JSM 2011 Online Program

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Abstract Details

Activity Number: 40
Type: Contributed
Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #302435
Title: Group Exponential Penalties for Bi-Level Variable Selection
Author(s): Patrick Breheny*+
Companies: University of Kentucky
Address: 121 Washington Ave., Lexington, KY, 40515,
Keywords: Group lasso ; Penalized regression ; Variable selection ; Genetic association ; Lasso ; MCP
Abstract:

In many applications, covariates possess a grouping structure that can be incorporated into the analysis to select important groups as well as important members of those groups. One important example arises in genetic association studies, where genes may have several variants capable of contributing to disease. The attributes that make a penalty desirable at the level of the individual variable are not necessarily the same on the group level. This work proposes a new, exponential penalty for combining together at the group level individual-variable penalties such as lasso and MCP. We demonstrate that this group exponential approach has advantages over previously proposed group penalties such as the group lasso and group MCP. Finally, we apply these methods to the genetic association study from GAW17.


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