Analysis of Gaussian Spatial Models with Covariate Measurement Error

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TADAYON VAHID; RASEKH ABDOLRAHMAN

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Journal: JOURNAL OF STATISTICAL SCIENCES Year:2019 | Volume:13 | Issue:1 Page(s): 77-97

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Information Journal Paper

Title

Analysis of Gaussian Spatial Models with Covariate Measurement Error

Author(s)

TADAYON VAHID | RASEKH ABDOLRAHMAN | Issue Writer Certificate

Keywords

Gaussian Spatial ModelQ1

Measurement ErrorQ2

Bayesian AnalysisQ1

Abstract

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.

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APA: Copy

TADAYON, VAHID, & RASEKH, ABDOLRAHMAN. (2019). Analysis of Gaussian Spatial Models with Covariate Measurement Error. JOURNAL OF STATISTICAL SCIENCES, 13(1 ), 77-97. SID. https://sid.ir/paper/124121/en

Vancouver: Copy

TADAYON VAHID, RASEKH ABDOLRAHMAN. Analysis of Gaussian Spatial Models with Covariate Measurement Error. JOURNAL OF STATISTICAL SCIENCES[Internet]. 2019;13(1 ):77-97. Available from: https://sid.ir/paper/124121/en

IEEE: Copy

VAHID TADAYON, and ABDOLRAHMAN RASEKH, “Analysis of Gaussian Spatial Models with Covariate Measurement Error,” JOURNAL OF STATISTICAL SCIENCES, vol. 13, no. 1 , pp. 77–97, 2019, [Online]. Available: https://sid.ir/paper/124121/en

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