The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
|
Activity Number:
|
314
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Biometrics Section
|
| Abstract - #300937 |
|
Title:
|
Two Artificial Mixture Methods for Discrete/Grouped Failure Time Data
|
|
Author(s):
|
Shufang Wang*+ and Alexander Tsodikov
|
|
Companies:
|
University of Michigan and University of Michigan
|
|
Address:
|
1420 washington heights, Ann Arbor, AL, 48109, US
|
|
Keywords:
|
discrete failure time ;
artificial mixture model ;
proportional odds model
|
|
Abstract:
|
we consider a general discrete transformation model for failure time data in a large data set with many ties by changing the model form at the "complete-data" level (conditional on artificial variables). Two complete data representations of a given discrete transformation model are studied: proportional hazards (PH) and proportional odds (PO) mixture methods. In PH mixture method, we reduce the high-dimensional optimization problem to many one-dimensional problems. In PO mixture method, a recursive procedure is available to simplify the optimization. As a result, we advocate the PO mixture method.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2011 program
|
2011 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.