Bayesian Analysis of Spatial Probit Models in Wheat Waste Management Adoption

Bayesian Analysis of Multivariate Probit Models

This paper provides a uni ed simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods, and maximum likelihood estimates are obtained by a Monte Carlo version of the E-M algorithm. Computation of Bayes factors from the simulation output is also considered. The met...

Symmetric Bayesian Multinomial Probit Models

Standard Bayesian multinomial probit (MNP) models that are fit using different base categories can give different predictions. Therefore, we propose the symmetric MNP model, which does not make reference to a base category. To achieve this, we employ novel sum-to-zero identifying restrictions on the latent utilities and regression coefficients that define the model. This results in a model whos...

A Bayesian Probit Model with Spatial Dependencies

A Bayesian probit model with individual effects that exhibit spatial dependencies is set forth. Since probit models are often used to explain variation in individual choices, these models may well exhibit spatial interaction effects due to the varying spatial location of the decision makers. That is, individuals located at similar points in space may tend to exhibit similar choice behavior. The...

Bayesian Prediction in Spatial Data Analysis

Crash Injury Severity Analysis Using Bayesian Ordered Probit Models

Understanding the underlying relationship between crash injury severity and factors such as driver’s characteristics, vehicle type, and roadway conditions is very important for improving traffic safety. Most previous studies on this topic used traditional statistical models such as ordered probit OP , multinomial logit, and nested logit models. This research introduces the Bayesian inference an...

Estimating Spatial Probit Models in R

In this article we present the Bayesian estimation of spatial probit models in R and provide an implementation in the package spatialprobit. We show that large probit models can be estimated with sparse matrix representations and Gibbs sampling of a truncated multivariate normal distribution with the precision matrix. We present three examples and point to ways to achieve further performance ga...