Abstract Details
Activity Number:
|
163
|
Type:
|
Topic Contributed
|
Date/Time:
|
Monday, August 4, 2014 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Statistical Computing
|
Abstract #311576
|
View Presentation
|
Title:
|
Sparse Regression Incorporating Graphical Structure Among Predictors
|
Author(s):
|
Guan Yu*+ and Yufeng Liu
|
Companies:
|
University of North Carolina at Chapel Hill and University of North Carolina at Chapel Hill
|
Keywords:
|
Prediction ;
Model Selection ;
Graph ;
Sparse Regression ;
Neighborhood ;
Lasso
|
Abstract:
|
With the abundance of high dimensional data in various disciplines, sparse regularized techniques are very popular these days. In this paper, we use the structure information among predictors to improve sparse regression models. Typically, such structure information can be modeled by the connectivity of an undirected graph. Most existing methods use this graph edge-by-edge to encourage the regression coefficients of corresponding connected predictors to be similar. However, such methods may require expensive computation when the predictor graph has many edges. Furthermore, they do not directly utilize the neighborhood information. In this paper, we incorporate the graph information node-by-node instead of edge-by-edge. To that end, we decompose the true regression coefficient as the sum of some latent parts and incorporate group penalty functions to use predictor graph neighborhood structure information. Our proposed method is quite general and it includes adaptive Lasso, group Lasso and ridge regression as special cases. Both theoretical study and numerical study demonstrate the effectiveness of the proposed method for simultaneous estimation, prediction and model selection.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.