Modeling unobserved heterogeneity of spatially correlated count data using finite-mixture random parameters

The main goal of this research is to propose a specification to model the unobserved heterogeneity in count outcomes. A negative binomial likelihood is utilized for modeling count data. Unobserved heterogeneity is modeled using random model parameters with finite multi-variate normal mixture prior structure. The model simultaneously accounts for potential spatial correlation of crash counts from neighboring units. The model extracts the inherent groups of road segments with crash counts that are equally sensitive to the road attributes on an average; the heterogeneity within these groups is also allowed in the proposed framework. This research employs a computationally efficient Bayesian estimation framework to perform statistical inference of the proposed model. A Markov Chain Monte Carlo (MCMC) sampling strategy is proposed that leverages recent theoretical developments in data-augmentation algorithms, and elegantly sidesteps many of the computational difficulties usually associated with Bayesian inference of count models.