Abstract Details
|
Activity Number:
|
692
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Section on Statistics in Epidemiology
|
| Abstract - #308809 |
|
Title:
|
Vertically Shifted Mixture Models for Clustering Longitudinal Data
|
|
Author(s):
|
Brianna Heggeseth*+ and Nicholas Jewell
|
|
Companies:
|
Williams College and University of California Berkeley
|
|
Keywords:
|
Clustering ;
Longitudinal Data ;
Shape
|
|
Abstract:
|
A key advantage of a longitudinal study is its ability to measure how individuals change over time. The majority of available methods to cluster this type of structured data do not group individuals based on the most interesting characteristic of the data: the shape of the trajectory over time. We discuss a few clustering methods that claim to target the shape and demonstrate how they fail on data with moderately large noise. Then we propose an alternative method that works well for simulated and real data sets such as childhood growth trajectories.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 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.
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.