Nonparametric Model For Poverty Data: The Effect of Internal Factors Using Multi-Predictor Spline Regression in Indonesia - Ruliana; Hidayat, Rahmat; Hardianti Hafid; Sudarmin (Jurnal MSA (Matematika dan Statistika serta Aplikasinya), 2025)
Poverty, as a multidimensional issue affecting national welfare and development, is the main focus of this research. This study investigates the impact of demographic and educational factors on the percentage of the poor population in Indonesia using a nonparametric Spline regression approach. The variables studied include the average population growth rate, the availability of schools in villages, and school enrollment rates. The best model, selected based on the lowest Generalized Cross Validation (GCV) value (0.204) and a high coefficient of determination (94.67%) is a nonparametric Spline regression model with an optimal combination of knot points. The analysis shows that all three predictor variables significantly influence the poverty rate. The model also meets standard statistical assumptions. These findings highlight the vital role of education and demographic factors in addressing poverty, thus strengthening education and controlling population growth should be a priority in poverty alleviation policies in Indonesia.