Bayesian Estimation of Spatio-Temporal Models with Covariates Measured with Spatio-Temporally Correlated Errors: Evidence from Monte Carlo Simulation
Mohammad Masjkur, Henk Folmer
Available Online February 2018.
- https://doi.org/10.2991/bcm-17.2018.61How to use a DOI?
- Spatio-temporal model, measurement error, Bayesian analysis
- Spatio-temporal data are susceptible to covariates measured with errors. However, little is known about the empirical effects of measurement error on the asymptotic biases in regression coefficients and variance components when measurement error is ignored. The purpose of this paper is to analyze Bayesian inference of spatio-temporal models in the case of a spatio-temporally correlated covariate measured with error by way of Monte Carlo simulation. We consider spatio-temporal model with spatio-temporal correlation structure corresponds to the Leroux conditional autoregressive (CAR) and the first order autoregressive priors. We apply different spatio-temporal dependence parameter of response and covariate. We use the relative bias (RelBias) and Root Mean Squared Error (RMSE) as valuation criteria. The simulation results show the Bayesian analysis considering measurement error show more accurate and efficient estimated regression coefficient and variance components compared with na‹ve analysis.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Mohammad Masjkur AU - Henk Folmer PY - 2018/02 DA - 2018/02 TI - Bayesian Estimation of Spatio-Temporal Models with Covariates Measured with Spatio-Temporally Correlated Errors: Evidence from Monte Carlo Simulation BT - 4th Bandung Creative Movement International Conference on Creative Industries 2017 (4th BCM 2017) PB - Atlantis Press SP - 313 EP - 317 SN - 2352-5428 UR - https://doi.org/10.2991/bcm-17.2018.61 DO - https://doi.org/10.2991/bcm-17.2018.61 ID - Masjkur2018/02 ER -