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September 16, 2025UNP Journal of Statistics and Data Science

Comparison of Kernel and Spline Nonparametric Regression (Case Study: Food Security Index of Jambi Province 2023)

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Authors

RARosa Salsabila AzarineSASeptrina Kiki ArisandiFFFadhilah Fitri

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Overview

This analysis compares nonparametric regression methods for food security index in 141 subdistricts of Jambi Province, highlighting the effectiveness of kernel regression.

Key Points

  • The Nadaraya-Watson Estimator outperformed other methods with the smallest mean squared error (MSE) of 24.47690.
  • Approximately 33.32% of the variation in food security index (FSI) was explained by food expenditure using the NWE method.
  • Smoothing Spline and B-Spline methods exhibited higher prediction error rates compared to kernel regression approaches.
  • The study utilized secondary data from the Food Security and Vulnerability Map (FSVA) of 2023, covering 141 subdistricts.

Cite This Study

Azarine et al. (2025) studied this question.

synapsesocial.com/papers/68d4508931b076d99fa5880fhttps://doi.org/10.24036/ujsds/vol3-iss3/397
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