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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Indonesian Journal of Applied Statistics Prosiding Seminar Nasional Official Statistics
Hasrat Ifolala Zebua
Badan Pusat Statistik Kabupaten Nias
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Mathematics Social Sciences

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Journal : Prosiding Seminar Nasional Official Statistics

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Pemodelan Kemiskinan di Sumatera Utara Menggunakan Regresi Nonparametrik Kernel dan Splines Hasrat Ifolala Zebua
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (243.746 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1087

Abstract

The Sustainable Development Goals' main goal is to reduce poverty (SDGs). Low human capital is the cause of poverty. The Human Development Index is one indicator that can be used to assess human capital (HDI). Despite having the largest population on the island of Sumatra, North Sumatra continues to have the fifth highest poverty rate. Because it is flexible and can model data at different levels, this study aims to model poverty with factors that influence it, namely HDI in North Sumatra using nonparametric regression and quantile regression. Kernel regression and smoothing splines are the nonparametric regression techniques used in this study. The optimal bandwidth of the gaussian kernel function with NWE was 2.13512 with GCV 11.78793, modeling with smoothing splines produced an optimal smoothing parameter value of 0.00544 with GCV 47.29301, and modeling with quantile regression smoothing splines produced an optimal smoothing parameter value of 0.11 with a GCV of 3.81497. The smoothing splines quantile regression method is the best method, according to the results of the model comparison, because it has a regression curve that follows the distribution of data relationships and lower GCV and RMSE values.
Co-Authors Geni Andalria Harefa I Gede Nyoman Mindra Jaya