Abstract Details
Activity Number:
|
80
|
Type:
|
Contributed
|
Date/Time:
|
Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
Abstract #312521
|
View Presentation
|
Title:
|
Variable Screening Under Dependence
|
Author(s):
|
Teng Zhang*+ and Jessie Jeng
|
Companies:
|
North Carolina State University and North Carolina State University
|
Keywords:
|
Variable screening ;
False negative control ;
Dependence ;
Data-driven ;
Dimension reduction
|
Abstract:
|
In ultra high dimensional study, variable screening is widely used in variable selection and dimension reduction. Practitioners in real applications often use subjective criterion to select a prefixed number of top-ranked variables to reduce the dimension of the data. However, how to efficiently determine the prefixed number remains an open question. In this paper, we provide a consistent estimator for the proportion of important variables under dependence. Based on the estimated proportion, a data-driven screening procedure is proposed to efficiently control the false negative at a desirable level. The proposed method is applied to several examples in genomic data analysis.
|
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.