A Bayesian Spatiotemporal Model for Multivariate Zero-Inflated Binomial Counts

Music City Center 

A spatiotemporal count data with known upper bounds modeled using the binomial distribution typically fails to account for the presence of excess zero counts. To address this, we propose a statistical methodology for such count data defined in areal units and discrete time. Through a Bayesian hierarchical framework, we build our model from three existing models-the multivariate zero-inflated binomial model for correlated counts, the Leroux model for spatial effects, and a nonparametric trend model for temporal effects. The inference for the parameters and hyperparameters is facilitated using Markov Chain Monte Carlo. We demonstrate the proposed model to the quarterly data on young adolescent birth counts in the areas in Luzon, Philippines from 2006 to 2019.

zero-inflated model

Bayesian hierarchical models

spatiotemporal analysis

multivariate count data

adolescent pregnancy 

Section on Bayesian Statistical Science