A Computatioal Analysis of Kernel-Based Nonparametric Regression Applied to Poverty Data
DOI:
10.29303/jm.v7i3.9802Published:
2025-09-05Downloads
Abstract
This research aims to model the relationship between poverty and socioeconomic variables in Nusa Tenggara Timur Province, Indonesia. The purpose of the study is to assess the effectiveness of nonparametric regression, specifically using kernel methods, to provide a more accurate representation of the complex and nonlinear relationships between predictor variables and poverty levels. The study focuses on several key variables, including average years of schooling, labor force participation rate, percentage of households with access to electricity, population density, illiteracy rate, and life expectancy. The research utilized a kernel regression approach, comparing the performance of different kernel functions, including Gaussian, Epanechnikov, Triangle, and Quartic kernels. The model’s performance was evaluated using metrics such as Mean Squared Error (MSE), Generalized Cross Validation (GCV), and the coefficient of determination (R²). The results showed that the Gaussian kernel function provided the most accurate predictions for poverty levels, with the best balance between model complexity and error.
Keywords:
Kernel Regression Nonparametric Regression Poverty Computation NTTReferences
Adrianingsih, N. Y., Budiantara, I. N., & Purnomo, J. D. T. (2021). Modeling with Mixed Kernel, Spline Truncated and Fourier Series on Human Development Index in East Java. IOP Conference Series: Materials Science and Engineering, 1115(1), 012024. https://doi.org/10.1088/1757-899x/1115/1/012024
Ali, T. H. (2022). Modification of the adaptive Nadaraya-Watson kernel method for nonparametric regression (simulation study). Communications in Statistics: Simulation and Computation, 51(2), 391–403. https://doi.org/10.1080/03610918.2019.1652319
Ameer Basheer, Z., Zakariya, D., & Algamal, Y. (2020). Smoothing parameter selection in Nadaraya-Watson kernel nonparametric regression using nature-inspired algorithm optimization. Iraqi Journal of Statistical Science, 32, 62–75.
Asfirah, N., Islamiyati, A., & Nirwan. (2025). The Use Of Likelihood-Based Threshold In Estimating Nonparametric Regression Models Through The Adaptive Nadaraya-Watson Estimator. Communications in Mathematical Biology and Neuroscience, 2025. https://doi.org/10.28919/cmbn/8911
Aydin, D., & Yilmaz, E. (2021). Censored Nonparametric Time-Series Analysis with Autoregressive Error Models. Computational Economics, 58(2), 169–202. https://doi.org/10.1007/s10614-020-10010-8
BPS. (2024). Ringkasan Data Dan Informasi Kemiskinan Provinsi NTT 2024. Badan Pusat Statistik Provinsi Nusa Tenggara Timur.
Bukhtoyarov, V. V., & Tynchenko, V. S. (2021). Design of computational models for hydroturbine units based on a nonparametric regression approach with adaptation by evolutionary algorithms. Computation, 9(8). https://doi.org/10.3390/computation9080083
Chen, H. (1991). Polynomial splines and nonparametric regression. Journal of Nonparametric Statistics, 1(1–2), 143–156. https://doi.org/10.1080/10485259108832516
Ciucu, A., Vargas, V., Păuna, C., & Jigani, A.-I. (2025). Poverty, Education, and Decent Work Rates in Central and Eastern EU Countries. Standards, 5(2), 16. https://doi.org/10.3390/standards5020016
Dani, A. T. R., Ratnasari, V., & Budiantara, I. N. (2021). Optimal Knots Point and Bandwidth Selection in Modeling Mixed Estimator Nonparametric Regression. IOP Conference Series: Materials Science and Engineering, 1115(1), 012020. https://doi.org/10.1088/1757-899x/1115/1/012020
Hardle, W. (1994). Applied Nonparametric Regression. In Humboldt-Universitat zu Berlin, Institut fur Statistik und Okonometrie. Humboldt-Universitat zu Belrin. https://doi.org/10.2307/2348990
Hidayat, R., Budiantara, I. N., Otok, B. W., & Ratnasari, V. (2019). Kernel-Spline Estimation of Additive Nonparametric Regression Model. IOP Conference Series: Materials Science and Engineering, 546(5). https://doi.org/10.1088/1757-899X/546/5/052028
Linke, Y., Borisov, I., Ruzankin, P., Kutsenko, V., Yarovaya, E., & Shalnova, S. (2022). Universal Local Linear Kernel Estimators in Nonparametric Regression. Mathematics, 10(15). https://doi.org/10.3390/math10152693
Mariati, N. P. A. M., Budiantara, I. N., & Ratnasari, V. (2020). Combination Estimation of Smoothing Spline and Fourier Series in Nonparametric Regression. Journal of Mathematics, 2020. https://doi.org/10.1155/2020/4712531
Marques, A. E., Prates, P. A., Pereira, A. F. G., Oliveira, M. C., Fernandes, J. V., & Ribeiro, B. M. (2020). Performance comparison of parametric and non-parametric regression models for uncertainty analysis of sheet metal forming processes. Metals, 10(4). https://doi.org/10.3390/met10040457
Masrur Ahmed, A. A., Sharma, E., Janifer Jabin Jui, S., Deo, R. C., Nguyen-Huy, T., & Ali, M. (2022). Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors. Remote Sensing, 14(5). https://doi.org/10.3390/rs14051136
Puttanapong, N., Martinez, A., Bulan, J. A. N., Addawe, M., Durante, R. L., & Martillan, M. (2022). Predicting Poverty Using Geospatial Data in Thailand. ISPRS International Journal of Geo-Information, 11(5). https://doi.org/10.3390/ijgi11050293
Rahmawati, D. P., Budiantara, I. N., Prastyo, D. D., & Octavanny, M. A. D. (2021a). Bi-response Spline Smoothing Estimator for Modelling the Percentage of Poor Population and Human Development Index in Papua Province. AIP Conference Proceedings, 2329. https://doi.org/10.1063/5.0042396
Rahmawati, D. P., Budiantara, I. N., Prastyo, D. D., & Octavanny, M. A. D. (2021b). Mixed Spline Smoothing and Kernel Estimator in Biresponse Nonparametric Regression. International Journal of Mathematics and Mathematical Sciences, 2021. https://doi.org/10.1155/2021/6611084
Ratnasari, V., Budiantara, N., & Dani, A. T. R. (2021). Nonparametric Regression Mixed Estimators of Truncated Spline and Gaussian Kernel based on Cross-Validation (CV), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) Methods. International Journal on Advanced Science Engineering Information Technology, 11(6), 2400–2406.
Seo, S. W., Choi, G., Jung, H. J., Choi, M. J., Oh, Y. D., Jang, H. S., Lim, H. K., & Jo, S. (2025). A Weighted Bayesian Kernel Machine Regression Approach for Predicting the Growth of Indoor-Cultured Abalone. Applied Sciences (Switzerland), 15(2). https://doi.org/10.3390/app15020708
Sugiharti, L., Purwono, R., Esquivias, M. A., & Rohmawati, H. (2023). The Nexus between Crime Rates, Poverty, and Income Inequality: A Case Study of Indonesia. Economies, 11(2). https://doi.org/10.3390/economies11020062
Usmanova, A., Aziz, A., Rakhmonov, D., & Osamy, W. (2022). Utilities of Artificial Intelligence in Poverty Prediction: A Review. In Sustainability (Switzerland) (Vol. 14, Issue 21). MDPI. https://doi.org/10.3390/su142114238
Wang, Y., Jia, S., Qi, W., & Huang, C. (2022). Examining Poverty Reduction of Poverty-Stricken Farmer Households under Different Development Goals: A Multiobjective Spatio-Temporal Evolution Analysis Method. International Journal of Environmental Research and Public Health, 19(19). https://doi.org/10.3390/ijerph191912686
Widyastuti, D. A., Fernandes, A. A. R., & Pramoedyo, H. (2021). Spline estimation method in nonparametric regression using truncated spline approach. Journal of Physics: Conference Series, 1872(1), 1–10. https://doi.org/10.1088/1742-6596/1872/1/012027
License
Copyright (c) 2025 ANDREA TRI RIAN DANI
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
