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Analysis of Gaussian Spatial Models with Covariate Measurement Error
Vahid Tadayon , Abdolrahman Rasekh
Abstract:   (1155 Views)

Uncertainty is an inherent characteristic of biological and geospatial data which is almost made by measurement error in the observed values of the quantity of interest. Ignoring measurement error can lead to biased estimates and inflated variances and so an inappropriate inference. In this paper, the Gaussian spatial model is fitted based on covariate measurement error. For this purpose, we adopt the Bayesian approach and utilize the Markov chain Monte Carlo algorithms and data augmentations to carry out calculations. The methodology is illustrated using simulated data.

Keywords: Gaussian Spatial Model, Measurement Error, Bayesian Analysis.
Full-Text [PDF 6651 kb]   (296 Downloads)    
Type of Study: Applied | Subject: Spatial Statistics
Received: 2016/03/19 | Accepted: 2018/06/29
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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