TY - CONF

T1 - Binary Mixed Effects Models and Improper Priors with Application in Meta-Analysis

AU - Chatterjee, Arpita

N1 - 2012 JSM Online Program Home For information, contact [email protected] or phone (888) 231-3473. If you have questions about the Continuing Education program, please contact the Education Department.
Arpita Chatterjee. "Binary Mixed Effects Models and Improper Priors with Application in Meta-Analysis" Contributed talk, Joint Statistical Meetings. San Diego. Jul. 2012.source:http://www.amstat.org/meetings/jsm/2012/onlineprogram/AbstractDetails.cfm?abstractid=306668

PY - 2012/7

Y1 - 2012/7

N2 - The most crucial part in Bayesian analysis is the choice of prior distribution. Improper priors are often used in hierarchical Bayesian models due to the lack of information on the hyper parameters at the lower levels of the hierarchy. When improper priors are used, it is important to establish the posterior propriety. Binary random/mixed effects models are commonly used in Meta analyses of binary outcome data. For severely sparse data the likelihood based estimates, obtained from such models, may tend towards the boundary, and this may hamper Bayesian computation and inference even under proper priors. We establish conditions for posterior propriety for such models. The random effects model we consider includes both parameters of interest and nuisance parameters, and the notion of posterior propriety in this model is linked to the notion of polyhedral cones and the idea of complete and quasi-complete separation in logistic regression. We further illustrate that, even in cases when the prior is diffuse, the Markov chain based computations and Bayesian inference may get adversely affected due to these limiting cases.

AB - The most crucial part in Bayesian analysis is the choice of prior distribution. Improper priors are often used in hierarchical Bayesian models due to the lack of information on the hyper parameters at the lower levels of the hierarchy. When improper priors are used, it is important to establish the posterior propriety. Binary random/mixed effects models are commonly used in Meta analyses of binary outcome data. For severely sparse data the likelihood based estimates, obtained from such models, may tend towards the boundary, and this may hamper Bayesian computation and inference even under proper priors. We establish conditions for posterior propriety for such models. The random effects model we consider includes both parameters of interest and nuisance parameters, and the notion of posterior propriety in this model is linked to the notion of polyhedral cones and the idea of complete and quasi-complete separation in logistic regression. We further illustrate that, even in cases when the prior is diffuse, the Markov chain based computations and Bayesian inference may get adversely affected due to these limiting cases.

KW - Binary random effects models

KW - Diffuse prior

KW - Improper prior

KW - Meta-analysis

KW - Posterior propriety

UR - http://ww2.amstat.org/meetings/jsm/2012/onlineprogram/AbstractDetails.cfm?abstractid=306668

M3 - Presentation

T2 - Joint Statistical Meetings (JSM)

Y2 - 12 August 2015

ER -