Pemodelan Geographically Weighted Logistic Regression dengan Metode Ridge

Abstract

One of the goals of national development is to reduce poverty. Poverty is included in the phenomenon of spatial heterogeneity because it can be shown by the varying economic conditions in each region. The statistical modeling method developed for data analysis takes into account regional factors namely Geographical Weighted Logistic Regression (GWLR). The parameter estimator of the GWLR semiparametric model used in this study was obtained using the Maximum Likelihood Estimation method. In GWLR, the assumption that must be fulfilled is the absence of multicollinearity. One method for dealing with multicollinearity is ridge regression involving the addition of a constant bias . The results obtained were the MSE value of the parameter estimator with the ridge method (707.77) smaller than the GWLR model before using the ridge (715.88). This shows that the ridge method is more effective if there are multicollinearity problems.