Estimating spatial probit models in R

Stefan Wilhelm*, Miguel Godinho de Matos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)

Abstract

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 gains through parallelization of the Markov Chain Monte Carlo approach.
Original languageEnglish
Pages (from-to)130-143
Number of pages14
JournalR Journal
Volume5
Issue number1
DOIs
Publication statusPublished - 2013
Externally publishedYes

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