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:
|
183
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 1, 2011 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
International Chinese Statistical Association
|
| Abstract - #302754 |
|
Title:
|
On Information Analysis of Large Categorical Data
|
|
Author(s):
|
Philip E. Cheng*+ and Keng-Min Lin and Juin-Wei Liou and Michelle Liou
|
|
Companies:
|
International Chinese Statistical Association and Academia Sinica and Academia Sinica and Academia Sinica
|
|
Address:
|
Institute of Statistical Science , Taipei , 115, TAIWAN
|
|
Keywords:
|
Linear Information Models ;
Log-linear Models ;
Model Selection ;
Mutual Information
|
|
Abstract:
|
Mutual information identities have provided an insight into statistical inference of categorical data. Recent studies by the authors showed that the fundamental likelihood structure presents information identities as linear information models (LIMs) directly observed from data. In contrast to the hierarchical log-linear models (LLM), LIM organize data information and select subsets of highly associated variables for useful dimension reductions out of a large multi-way contingency table. Parsimonious LIMs can be formulated and tested for the target variables of interest. An application of LIM is examined against a large and sparse banking credit-card risk categorical data of twenty-six variables, and various data interpretations are tested.
|
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.