Abstract Details
Activity Number:
|
376
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Statistical Computing
|
Abstract - #309025 |
Title:
|
Modeling Overdispersion in Binomial Data with Regression Linked to a Finite Mixture Probability of Success
|
Author(s):
|
Andrew Raim*+ and Nagaraj Neerchal
|
Companies:
|
University of Maryland, Baltimore County and University of Maryland, Baltimore County
|
Keywords:
|
Overdispersion ;
GLM ;
Logistic Regression ;
Random Effects ;
Goodness-of-Fit
|
Abstract:
|
Logistic regression often cannot account for large variability seen in binomial data due to departures from standard assumptions. Many techniques have been considered to address this issue, commonly known as overdispersion. Finite mixture distributions may be used when the extra variation is explained by the presence of several latent subpopulations. For example, a finite mixture of regressions links the probability for each latent group to a seperate regression. Analogously to the usual logistic regression, we consider linking a regression to the mixture probability of success in a finite mixture of binomials. This can be seen as ``marginal modeling' with respect to the latent groups, as opposed to the mixture of regressions which is seen as ``conditional modeling' on the groups, and would allow more parsimonious models when only a single overall regression is desired. Our approach is likelihood-based, which may be considered an advantage over quasi-likelihood techniques often used to address overdispersion. This work presents the new model and an illustrative example.
|
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.