RT - Journal Article T1 - Bayesian Analysis of Censored Spatial Data Based on a Non-Gaussian Model JF - srtc-jsri YR - 2017 JO - srtc-jsri VO - 13 IS - 2 UR - http://jsri.srtc.ac.ir/article-1-241-en.html SP - 155 EP - 180 K1 - Censored data K1 - data augmentation K1 - non-Gaussian spatial models K1 - outlier K1 - unified skew Gaussian. AB - Abstract: In this paper, we suggest using a skew Gaussian-log Gaussian model for the analysis of spatial censored data from a Bayesian point of view. This approach furnishes an extension of the skew log Gaussian model to accommodate to both skewness and heavy tails and also censored data. All of the characteristics mentioned are three pervasive features of spatial data. We utilize data augmentation method and Markov chain Monte Carlo (MCMC) algorithms to do posterior calculations. The methodology is illustrated using simulated data, as well as applying it to a real data set. LA eng UL http://jsri.srtc.ac.ir/article-1-241-en.html M3 10.18869/acadpub.jsri.13.2.155 ER -