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:
|
406
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract - #302873 |
Title:
|
Hierarchical Bayesian Random Sets with Applications to Growth Models
|
Author(s):
|
Athanasios Micheas*+
|
Companies:
|
University of Missouri at Columbia
|
Address:
|
Department of Statistics, , MO, 65211 ,
|
Keywords:
|
Random Object ;
Finite Mixture Models ;
Hierarchical Bayesian Model ;
Growth Models ;
Data Augmentation ;
Reversible Jump MCMC
|
Abstract:
|
We propose and study a novel Bayesian Hierarchical framework to model objects stochastically as well as capture the growth or evolution of an object, based on random sets. We efficiently model a random set via a multistage hierarchical Bayesian framework, and using finite mixture models of possibly varying dimension. Such models require the use of data augmentation techniques as well as Reversible Jump MCMC sampling methods. Several growth models are proposed, including Hereditary, Birth-Death and Mixed type, as well as the corresponding Bayesian formulation that provides inference and prediction. The models are exemplified through some simulations.
|
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.