Prosiding Seminar Nasional Official Statistics
Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021

Pemodelan Kemiskinan di Sumatera Utara Menggunakan Regresi Nonparametrik Kernel dan Splines

Hasrat Ifolala Zebua (Unknown)



Article Info

Publish Date
01 Nov 2021

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.

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Journal Info

Abbrev

semnasoffstat

Publisher

Subject

Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

Description

prosiding seminar ini bertujuan untuk menghasilkan berbagai pemikiran solutif, inovatif, dan adaptif terkait isu, strategi, dan metode yang memanfaatkan official ...