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

Research Article

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

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  • @INPROCEEDINGS{10.4108/eai.2-8-2019.2290526,
        author={Mohammad  Masjkur and Henk  Folmer and Asep  Saefuddin},
        title={Comparison of MCEM and Bayesian Correction Methods of Spatially Lagged Covariates Measured with Error : Evidence from Monte Carlo Simulation},
        proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia},
        publisher={EAI},
        proceedings_a={ICSA},
        year={2020},
        month={1},
        keywords={bayesian approach mcem measurement error spatial regression},
        doi={10.4108/eai.2-8-2019.2290526}
    }
    
  • Mohammad Masjkur
    Henk Folmer
    Asep Saefuddin
    Year: 2020
    Comparison of MCEM and Bayesian Correction Methods of Spatially Lagged Covariates Measured with Error : Evidence from Monte Carlo Simulation
    ICSA
    EAI
    DOI: 10.4108/eai.2-8-2019.2290526
Mohammad Masjkur1,*, Henk Folmer2, Asep Saefuddin1
  • 1: Department of Statistics, Bogor Agricultural University, Bogor, Indonesia
  • 2: Faculty of Spatial Sciences, University of Groningen, The Netherlands
*Contact email: masjkur@apps.ipb.ac.id

Abstract

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.