Abstract Details
Activity Number:
|
433
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract - #309090 |
Title:
|
Approximate Bayesian Computation for a Flexible Class of Bivariate Beta Distributions
|
Author(s):
|
Roberto Crackel*+ and James M. Flegal
|
Companies:
|
UC Riverside and University of California, Riverside
|
Keywords:
|
approximate Bayesian computation ;
MH algorithm ;
bivariate beta ;
accept-reject
|
Abstract:
|
Several bivariate beta distributions have been proposed in the literature. In particular, Olkin and Liu (2003) proposed a 3 parameter bivariate beta model, while Arnold and Ng (2011) proposed a 5 parameter model. The 3 parameter model allows for only positive correlation, while the latter can accommodate both positive and negative correlation; however it comes at the expense of a density that is mathematically intractable. Arnold and Ng discuss an extension to an 8 parameter case.
The focus of this research is on Bayesian estimation for the 5 and 8 parameter model. Since the likelihood does not exist in closed form, we apply approximate Bayesian computation (ABC), a likelihood free based approach. We consider two particular algorithms encompassed within the ABC framework: the accept-reject method and the ABC-Metropolis Hastings algorithm. Simulation studies have been carried out for the 5 and 8 parameter cases under various priors and tolerance levels. Results and comparisons are discussed between the two proposed algorithms.
|
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.