Analysis of Gaussian Spatial Models with Covariate Measurement Error

Tadayon, Vahid; Rasekh, Abdolrahman

doi:10.29252/jss.13.1.77

Cited by 1 publication

(2 citation statements)

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“…[3]'s simulation revealed better performance of MCEM approach compared to naive approach measurement error model. [4] using Bayesian approach analyzed Gaussian spatial model with covariate measurement error under an exponential spatial structure. Unlike [1], [4] consider the functional model, i.e., a fixed true values of covariate.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of MCEM and Bayesian Correction Methods of Spatially Lagged Covariates Measured with Error : Evidence from Monte Carlo Simulation

Masjkur¹,

Folmer²,

Saefuddin³

2020

Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bog

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Measurement errors in (spatially lagged) explanatory variables under the classical-errors-in variables assumption are not routinely accounted for in applied (spatial) research, in spite of their serious consequences. Particularly, the estimator of coefficients of variables measured with error but also of those not measured with error are biased and inconsistent. The purpose of this paper is to analyze and compare by way of Monte Carlo simulation two bias correction methods, i.e. Monte Carlo Expectation-Maximization (MCEM) and Bayesian approach (BA). We consider spatial lag model (SLX) with different spatial correlation of covariate of interest, different measurement error variances and sample sizes. We use relative bias (RelBias) and Root Mean Squared Error (RMSE) as valuation criteria. The main result is that the Bayesian approach and MCEM method outperform the Naive model without measurement error correction. Moreover, the Bayesian approach performs better than MCEM method.

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Section: Introductionmentioning

confidence: 99%

“…[4] using Bayesian approach analyzed Gaussian spatial model with covariate measurement error under an exponential spatial structure. Unlike [1], [4] consider the functional model, i.e., a fixed true values of covariate. The simulation found that the proposed measurement error model has a better performance than naive model.…”

Section: Introductionmentioning

confidence: 99%