A Modified Spatial Variance Shift Outlier Model
DOI:
https://doi.org/10.62054/ijdm/0302.22Abstract
The Variance Shift Outlier Model (VSOM) and its spatial extension (SVSOM) for outlier accommodation have been developed in linear and spatial frameworks; however, they often fail to account simultaneously for spatial disparities and correlated measures within groups. This study proposes a Modified Spatial Variance Shift Outlier Model (m-SVSOM) within a Generalized Linear Mixed Model (GLMM) framework to address these limitations. The proposed model incorporates spatially lagged dependent variables and spatial autocorrelation in the residuals, while accounting for random effects to capture repeated measures. Variance parameter estimation is performed using the Restricted Maximum Likelihood (REML) method. To evaluate the model's performance, a simulation study was conducted across varying sample sizes (n = 16, 36, 144, 400, 900, 1000). The results show that the m-SVSOM consistently outperforms the SVSOM, achieving lower Mean Squared Error (MSE = 0.0522) and Root Mean Squared Error (RMSE = 0.2108). The findings suggest that the proposed model yields more robust parameter estimates, making it a valuable tool for analyzing complex spatial datasets with hierarchical structures.
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