A BAYESIAN PROBIT MODEL WITH SPATIAL DEPENDENCIES

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A latent variable representation of count data models to accommodate spatial and temporal dependence: application to predicting crash frequency at intersections (2011, background): “…4 Many studies attempt to side-step the high dimensional problem by clustering observation units into “regions”, and then considering a spatial error dependency over the regions rather than the observational units (see Smith and LeSage, 2004 and Phaneuf and Palmquist, 2003).”
Modeling Interdependent Consumer Preferences (2003, methodology): “We adopted an efficient method of generating θ from its posterior distribution while avoiding inverting a high-dimensional matrix that Smith and LeSage (2000) introduce.”
Thirty years of spatial econometrics (2010, background): “Models for spatial latent variables, in particular the spatial probit and spatial tobit models are further explored by LeSage (2000); Kelejian and Prucha (2001) (generalizing Moran’s I), Beron et al. (2003); Fleming (2004); and Smith and LeSage (2004), among others.”