Modeling the Percentage of Poor Population in Sulawesi Island Using Kernel Estimation in Priestley-Chao Semiparametric Regression - Ampa, Andi Tenri; Makkulau, Andi Tenri Pannangngareng; Ome, Lilis La; Ihwal, Muhammad; Yahya, Irma; Makkulau, Makkulau (Journal of Mathematics, Computations and Statistics, 2025)
This study aims to model the data on the Percentage of Poor Population in Sulawesi Island in 2023, considering various factors that influence poverty. Eradicating extreme poverty has become a top priority to be achieved by 2030. This study examines the influence of several variables, such as Open Unemployment Rate, Human Development Index, Labor Force Participation Rate, Average Length of Schooling, Percentage of Access to Proper Sanitation, and Gross Regional Domestic Product, on the Percentage of Poor Population in Sulawesi Island, using the Kernel Priestley-Chao estimation in Semiparametric regression with an Ordinary Least Square approach. This study also applies the selection of optimal bandwidth using the minimum Generalized Cross Validation method with an optimal bandwidth of 0.991, resulting in a Mean Absolute Percentage Error value of 16.32%. The model shows excellent estimation results, with a residual coefficient value of 69% used to model the Percentage of Poor Population data with a high level of accuracy. The data used partially has a parametric pattern, while some do not have a specific pattern, and there are outliers.