BAYESIAN MODELING OF CORRELATED BINARY RESPONSES VIA SCALE MIXTURE OF MULTIVARIATE NORMAL LINK FUNCTIONS
M.-H. Chen, Dipak K. Dey · Sankhya. Series A · 1998
In this article, we consider using scale mixture of multivariate normal links (SMMVN) to model binary responses when binary observations are taken from the same individuals or are taken over time in a longitudinal fashion. SMMVN-links are quite rich, which include multivariate probit, Student's t links, logit, symmetric stable link, and exponential power link. Fully parametric classical approaches to these are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theory involved in Bayesian modeling and computation is provided. In particular, we produce a new look at the multivariate logit model, the most popular model in this context. Further, we develop various efficient computational algorithms for this complex simulation problem. Finally, a real data example from the Indonesian Children's Health Study is used to illustrate the proposed methodology.