Research Article
Simulation Study for Comparison of Maximum Likelihood and Bayesian Method in Spatial Autoregressive Models with Heteroskedasticity
@INPROCEEDINGS{10.4108/eai.2-8-2019.2290484, author={Fitri Ramadhini and Anik Djuraidah and Aji Hamim Wigena}, title={Simulation Study for Comparison of Maximum Likelihood and Bayesian Method in Spatial Autoregressive Models with Heteroskedasticity}, 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 heteroskedasticity maximum likelihood spatial dependence spatial regression}, doi={10.4108/eai.2-8-2019.2290484} }- Fitri Ramadhini
Anik Djuraidah
Aji Hamim Wigena
Year: 2020
Simulation Study for Comparison of Maximum Likelihood and Bayesian Method in Spatial Autoregressive Models with Heteroskedasticity
ICSA
EAI
DOI: 10.4108/eai.2-8-2019.2290484
Abstract
Generally spatial regression considers only one of the spatial effects, namely spatial dependence or heteroskedasticity between areas. Spatial autoregressive (SAR) models take only into account the dependence on the response variable. Most of SAR estimators are valid if there is no violation in the error assumption. Estimation of SAR parameters with heteroskedasticity using maximum likelihood (ML) method gives bias and inconsistent estimators. An alternative method that can be used is Bayesian method. Bayesian method solves heteroskedasticity by modeling the structure of variance-covariance matrix. Simulation data is used to evaluate the Bayesian method in estimating parameters of SAR model with heteroskedasticity. The results indicate that Bayesian method provides bias parameter estimates relatively small and consistent compared to the ML method.
