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...

is equivalent to probit, logit, and related models. However, the formulation that assumes latent variable Zi is allowing Gibbs sampling scheme (eg., Chib and Albert 1993) and Johnson and Albert (1999)). Successive sampling from full conditionals, (i) [β|Z, Y ] and (ii) [Z|β, Y ]. Assume that F is normal distribution and that the above model is probit. Then the distribution for β given Z is simp...

In many social science researches and econometric applications data that arise through measurement of discrete outcomes or discrete choice among a set of alternatives are in the form of ordinal or ordered categorical data. Such examples, among others, are selfreport responses in household surveys, modeling labor force participate, or decision of which product to choose or which candidate to ele...

Non-Gaussian state-space models arise in several applications, and within this framework the binary time series setting provides a relevant example. However, unlike for Gaussian - where filtering, predictive smoothing distributions are available closed form require approximations or sequential Monte Carlo strategies inference prediction. This is due to apparent absence of conjugacy between stat...

In recent years, the skew-normal models introduced in Azzalini (1985) have enjoyed an amazing success, although an important literature has reported that they exhibit, in the vicinity of symmetry, singular Fisher information matrices and stationary points in the profile log-likelihood function for skewness, with the usual unpleasant consequences for inference. For general multivariate skew-symm...