Modeling of Human Development Index in Papua Province Using Spline Smoothing Estimator in Nonparametric Regression

• Published in: Journal of physics. Conference series 2021-02, Vol.1752 (1), p.12018
• Main Authors: Rahmawati, D P, Budiantara, I N, Prastyo, D D, Octavanny, M A D
• Format: Article
• Language: English
• Subjects: Economic development; Human Development Index; Model testing; Modelling; Multivariable Nonparametric Regression; Penalized Least Square; Physics; Population growth; Regression; Smoothing; Spline Smoothing
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1752/1/012018

The development goal of a country must be focused on the quality of human life to achieve prosperity. One important indicator for measuring the success of a country's development is the Human Development Index (HDI). In 2018, Papua was the province with the lowest HDI in Indonesia. Special attention is needed to improve HDI in Papua Province, one of them is by paying attention to the variables that affect HDI such as population growth rate, percentage of poor population, and economic growth. The relationships between HDI and the predictor variables do not have a clear pattern and tend to change at certain subintervals. This case can be approached using Spline Smoothing in multivariable nonparametric regression. Spline Smoothing is a type of estimator in nonparametric regression that has an excellent ability to handle data that tend to change at certain subintervals. Therefore, the purposes of this study are to obtain the form of Spline Smoothing estimator function in multivariable nonparametric regression, estimate the function and apply it to the HDI in Papua Province. The empirical results of modeling HDI in Papua Province show that it can be adequately applied which gives GCV = 58.108, R2 = 99.77% and RMSE = 0.0505.