Pemodelan Mixed Geographically Weighted Regression yang Mengandung Multikolinearitas dengan Regresi Ridge - Suritman; Raupong; Anisa Kalondeng (ESTIMASI: Journal of Statistics and Its Application, 2023)
In the Mixed Geographically Weighted Regression (MGWR) model, some variables are local and some are global. In MGWR modeling, it is often found that the data have multicollinearity. To overcome this problem, MGWR models with ridge regression are used. The MGWR model can be applied to poverty cases because it can experience spatial heterogeneity due to differences in geographical, cultural, and economic policies that vary in each region. In this study, the estimation of MGWR model parameters with ridge regression is then applied to data on the poor population of South Sulawesi in 2016. Data on the poor population of South Sulawesi experience multicollinearity, so it is solved using the MGWR model with ridge regression. Variables that have a significant effect globally are x3 and x6. while the variables that have a significant local effect are x2, x4, x5, x7, x8, x9 and x10. The AIC value of the MGWR model with ridge regression of 63.64473 is smaller than the MGWR model, meaning that the addition of ridge regression to the MGWR model makes the model better at overcoming multicollinearity problems.