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September 10, 2025Open Access

Evaluating Robust Estimators in Geographically Weighted Regression for Stunting Analysis at the District-Level Across Java: A Focus on Outlier Handling

Key Points

Robust estimators improved analysis of stunting prevalence across Java while addressing outliers effectively.
The analysis indicated that region-specific interventions may be key to achieving the national target of reducing stunting to 14%.
This study utilized the 2018 Riskesdas data with RGWR, a method designed to enhance geographical analysis by managing outliers.
Tailoring stunting reduction policies to local factors is crucial for effective intervention and regional progress.

Abstract

Rate remains high and lags behind neighboring countries such as Vietnam and Thailand. This slow progress underscores the need for region-specific interventions and a deeper understanding of local factors driving stunting to meet the 14% national target. This study applies RGWR, an improvement over GWR for handling outliers. This method uses M, S, and MM estimators applied to the analysis of the prevalence of stunting among children under five the 2018 Riskesdas data across 85 districts in Java. Immunization reduces disease risk, growth monitoring detects stunting early, ARI management mitigates disease impact, parental height influences stunting risk, and working mothers improve family income and healthcare access, all contributing to reduced stunting. Given the regional variation in impact, stunting reduction policies should be spatially tailored, the MBG program should be prioritized in eastern Java regions.

Evaluating Robust Estimators in Geographically Weighted Regression for Stunting Analysis at the District-Level Across Java: A Focus on Outlier Handling

Key Points

Abstract

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