MODELING GENDER DEVELOPMENT INDEX IN SOUTHEAST SULAWESI PROVINCE USING SEMIPARAMETRIC KERNEL REGRESSION

  • Andi Tenri Ampa Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Halu Oleo, Indonesia https://orcid.org/0009-0006-7010-8908
  • Lilis Laome Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Halu Oleo, Indonesia https://orcid.org/0000-0001-8433-1621
  • Muhammad Ridwan Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Halu Oleo, Indonesia https://orcid.org/0009-0002-5805-9593
  • Baharuddin Baharuddin Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Halu Oleo, Indonesia https://orcid.org/0009-0001-5777-3181
  • Makkulau Makkulau Statistics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Halu Oleo, Indonesia https://orcid.org/0009-0006-4090-3409
Keywords: Bandwidth, Gender Development Index, Nadaraya-Watson Estimator, Semiparametric Regression

Abstract

The issue of gender equality in Southeast Sulawesi still needs further attention, as indicated by the uneven value of the Gender Development Index (GDI) in each district/city in the region. Therefore, an in-depth analysis is needed to identify factors that affect the GDI. One method that can be used is semiparametric regression with the Nadaraya-Watson estimator, which allows modeling the relationship between variables with more flexibility. This study aims to build a semiparametric regression model to identify factors that contribute to HDI in Southeast Sulawesi Province. The results of the analysis showed that the optimal bandwidth values obtained were h1= 1.57, h2=0.49, h3=2.50 and h4=4.61. The resulting model has an R2 and MSE values of 99.8% and 0.14% respectively, indicating that the model has high accuracy in explaining the overall variation in GDI.

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Published
2025-07-01
How to Cite
[1]
A. T. Ampa, L. Laome, M. Ridwan, B. Baharuddin, and M. Makkulau, “MODELING GENDER DEVELOPMENT INDEX IN SOUTHEAST SULAWESI PROVINCE USING SEMIPARAMETRIC KERNEL REGRESSION”, BAREKENG: J. Math. & App., vol. 19, no. 3, pp. 1525-1536, Jul. 2025.