Geographically Weighted Regression with Spline Approach

Abstract

Geographically weighted truncated spline nonparametric regression is a new method of statistical science. It is used to solve the problems of regression analysis of spatial data whose regression curve is unknown. This method is the development of nonparametric regression with truncated spline function approach to the analysis of spatial data. Truncated spline approach can be a solution for the problem of modeling spatial data analysis. The data patterns between the response variable and the predictor variable are unknown or regression curve is not known. This study is focused on finding estimator of truncated spline nonparametric regression in geographically weighted regression models with weighted maximum likelihood estimator (MLE) method. The characteristic of the unbiased estimator is also investigated. The results show that the nonparametric regression with truncated spline function approach can be used to solve the problems of regression curve that cannot be identified in the spatial data and the results of the model find the unbiased estimator of the parameter.