Mixed Geographically Weighted Regression Modeling Using the MM-Estimator Method on Data of Poverty - Amalia Mentari Djalumang; Raupong, Raupong; Siswanto, Siswanto (Jurnal Varian, 2025)
The mixed geographically weighted regression model combines a global linear regression model with a geographically weighted regression model, with some parameters global and others local. When analyzing data with this model, outliers are common, which can significantly affect the regression coefficients and lead to biased parameter estimates. Therefore, a more robust estimation method that is resistant to outliers is needed to improve accuracy. This study aims to estimate the parameters of the mixed geographically weighted regression model using the Method of Moments (MM) Estimator method, which is more robust to outliers, and to identify the factors that significantly influence the percentage of the poor population in South Sulawesi Province in 2023. The results show that the poverty depth index has a significant global effect on the percentage of the population living in poverty. Meanwhile, the percentage of the population, the open unemployment rate, and the expected years of schooling have significant local effects. Based on these findings, it can be concluded that neighboring regions share common factors influencing poverty rates. These findings can assist policymakers in designing povertyalleviation programs that account for regional differences and support further research on robust spatial modeling approaches.