Model Regresi Nonparametrik dengan Pendekatan Deret Fourier Pada Kasus Tingkat Kemiskinan di Sulawesi Selatan
Abstract
This research discusses nonparametric Fourier series regression on poverty levels in South Sulawesi. The nonparametric regression approach is used when the curve pattern is not known or detailed information about the shape of the regression function. The Fourier series is a curve that shows the sine and cosine functions. The advantage of nonparametric Fourier series regression is that it can explain repeated data patterns. This research uses the GCV method to determine optimal K. The aim of this research is to obtain the best non-parametric regression model using a Fourier series approach to the factors that influence the level of poverty in South Sulawesi. The results of this research show that the best model is with an oscillation point K=3, which has a GCV value of 5.37, MSE of 2.38 and R2 of 68.9% and there is one parameter of the independent variable that has a significant effect on the number of poor people. namely the Human Development Index variable.
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