Abstract Details
Activity Number:
|
240
|
Type:
|
Contributed
|
Date/Time:
|
Monday, August 4, 2014 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
Abstract #313128
|
View Presentation
|
Title:
|
ThrEEBoost: Thresholded Boosting for Variable Selection and Prediction via Estimating Equations
|
Author(s):
|
Julian Wolfson*+ and Christopher Miller
|
Companies:
|
University of Minnesota and NAMSA
|
Keywords:
|
boosting ;
estimating equations ;
variable selection ;
prediction
|
Abstract:
|
Most variable selection techniques assume relatively simple settings where observations are independent and completely observed. In contrast, there is a rich literature on approaches to low-dimensional parameter estimation which handle the myriad complexities of ``real'' data, including correlation, missingness, measurement error, selection bias, etc. We present ThrEEBoost, a flexible algorithm for prediction and variable selection based on estimating equations. A generalization of EEBoost (Wolfson, 2011), ThrEEBoost uses a modified boosting technique in which multiple regression coefficients may be updated in a single iteration. The number of coefficients updated at each step is controlled by a thresholding parameter; different thresholding parameter values yield different variable selection paths, and the optimal degree of thresholding can be chosen by cross-validation. We show via simulation that ThrEEBoost performs well in both sparse and non-sparse settings, and offers substantial computational savings over competing L1-regularized methods.
|
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.