Abstract Details
|
Activity Number:
|
159
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Monday, August 4, 2014 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Biometrics Section
|
|
Abstract #312656
|
|
|
Title:
|
A Robust Method for Correlated RNA Sequence Data
|
|
Author(s):
|
Jinfeng Xu*+ and Hong Zhang
|
|
Companies:
|
and Fudan University
|
|
Keywords:
|
Count data ;
RNA ;
Poisson
|
|
Abstract:
|
We propose a model for analyzing correlated RNA sequence data by specifying a general mean-variance formula. It naturally extends the existing models and provides a more flexible tool. Fast algorithms are developed for its numerical implementation. Extensive simulation studies show that the approach exhibits superior robustness properties and perform favorably under a variety of settings. However, the competing approaches perform well only when their restrictive assumptions are met. In the real data applications, the new method also yields more interesting results.
|
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.