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

## TL;DR

This paper introduces a Bayesian non-Gaussian spatial model using a skew Gaussian-log Gaussian distribution to effectively analyze censored spatial data with skewness and heavy tails, employing MCMC for inference.

## Contribution

It extends the skew log Gaussian model to handle censored data and heavy tails in spatial analysis, providing a new Bayesian framework with data augmentation and MCMC methods.

## Key findings

- Effective modeling of censored spatial data with skewness and heavy tails.
- Successful application to simulated and real datasets.
- Demonstrates improved inference over traditional Gaussian models.

## 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. Keywords: Censored data, data augmentation, non-Gaussian spatial models, outlier, unified skew Gaussian.