Construction of a geographically weighted nonparametric regression model fit test

Abstract

One of the approach of Geographically Weighted Regression (GWR) models is the Geographically Weighted Nonparametric Regression (GWNR) has more parameters than the GWR model. Models with more parameters usually have better match values, which is an advantage, while models with fewer parameters have the advantage of being easier to use and interpret. However, a model with more parameters should be used if it is proven to be significantly superior. Therefore, the purpose of this study was to develop a hypothesis test of goodness of fit test for GWNR model. The goodness of fit test was performed for the real data. We found that the GWNR model was more suitable than the mixed nonparametric regression model. Some highlights of the proposed method are:•A new model for GWR to overcome the unknown regression function by using mixed estimator spline truncated and fourier series at nonparametric regression•Goodness of fit for GWNR to testing the model fit between the mixed nonparametric regression model and GWNR•Applied goodness of fit test to poverty data in Sulawesi Island and infant mortality in East Java