Abstract Details
Activity Number:
|
596
|
Type:
|
Topic Contributed
|
Date/Time:
|
Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Biometrics Section
|
Abstract #311870
|
|
Title:
|
A Split-and-Merge Bayesian Variable Selection Approach for Ultra-High-Dimensional Regression
|
Author(s):
|
Faming Liang*+ and Qifan Song
|
Companies:
|
Texas A&M and Texas A&M
|
Keywords:
|
Big Data ;
Markov chain Monte Carlo ;
Variable Selection
|
Abstract:
|
This talk presents a Bayesian variable selection approach for ultra-high dimensional linear regression based on the strategy of split-and-merge. The proposed approach consists of two stages: (i) split the ultra-high dimensional dataset into a number of lower dimensional subsets and select relevant variables from each of the subsets, and (ii) aggregate the variables selected from each sub- set and then select relevant variables from the aggregated dataset. Since the proposed approach has an embarrassingly parallel structure, it can be easily implemented in a parallel architecture and applied to big data problems with millions or more of explanatory variables. Under mild conditions, we show that the proposed approach is consistent; that is, the true explanatory variables can be correctly identified by the proposed approach as the sample size becomes large. Extensive comparisons of the proposed approach have been made with the penalized likelihood approaches, such as Lasso, elastic net, SIS and ISIS. The numerical results show that the proposed approach generally outperforms the penalized likelihood approaches: The models selected by the proposed approach tend to be more sparse and cl
|
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.