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In this paper, we suggest using a skew Gaussian-log Gaussian model for theanalysis of spatial censored data from a Bayesian point of view. This approachfurnishes an extension of the skew log Gaussian model to accommodate to bothskewness and heavy tails and also censored data. All of the characteristicsmentioned are three pervasive features of spatial data. We utilize dataaugmentation method and Markov chain Monte Carlo (MCMC) algorithms to doposterior calculations. The methodology is illustrated using simulated data, aswell as applying it to a real data set. Keywords: Censored data, dataaugmentation, non-Gaussian spatial models, outlier, unified skew Gaussian.

Bayesian Analysis of Censored Spatial Data Based on a Non-Gaussian Model