This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
|
Activity Number:
|
81
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
|
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
| Abstract - #306390 |
|
Title:
|
Model Selection for Dependent Data via the Minimum Description Length Principle
|
|
Author(s):
|
Li Li*+ and Radu Craiu and Fang Yao
|
|
Companies:
|
University of Toronto and University of Toronto and University of Toronto
|
|
Address:
|
Department of Statistics, Toronto, ON, M5S 3G3, Canada
|
|
Keywords:
|
Dependent Data ;
Minimum Description Length Principle ;
Linear Mixed Model ;
Functional Data Analysis
|
|
Abstract:
|
We propose using minimum description length (MDL) principle to select from a class of models for dependent data. Theoretical derivations and justifications of the MDL criterion are explored for linear mixed models and for functional data models in which the within-subject correlation is taken into account. The simulation results illustrate that the method is effective in choosing the correct model in both instances.
|
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 2010 program
|
2010 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.