Georaphically Weighted Ridge Regression Modelling on 2023 Poverty Indicators Data in the Provinces of West Kalimantan and Central Kalimantan

Anjani, Syarli Dita
Widiarti
Utami, Bernadhita Herindri Samodera
Usman, Mustofa
Handayani, Vitri Aprilla

Integra: Journal of Integrated Mathematics and Computer Science
Vol. 1 No. 3 (2024): November

Regression analysis is a method to explain the relations between independent variables and a dependent variable. Linear regression analysis relies on certain assumptions, one of the assumption is homogeneity. However, there is a situation when the variance at each observation differs or called spatial heterogeneity.This issue can be solved using Geographically Weighted Regression (GWR), a statistical method that can be fixed spatial heterogeneity by adding a local weighted matrix, the result in GWR model is a local model for each observation point. However, GWR has a limitation, it cannot handle multicollinearity. Ridge regression is a method used to solved multicollinearity by adding a bias constant (λ). A GWR model that contains multicollinearity and fixed using ridge regression is known as Geographically Weighted Ridge Regression (GWRR).