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

Silfiana Lis Setyowati, Muhammad Nur Aidi, Utami Dyah Syafitri, Fitrah Ernawati

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.

Keywords


Robust Geographically Weighted Regression; M estimator; S estimator; MM estimator; Prevalence of Toddler Stunting; District Level Stunting.

Full Text:

PDF

References


Ministry of Health, "Indonesia Health Survey (IHS) 2023 In Figures: Accurate Data for the Right Policy," Jakarta: BPKP, 2023.

G. Nduwayezu, C. Kagoyire, P. Zhao, L. Eklund, P. Pilesjo, J. Bizimana, and A. Mansourian, "Spatial Machine Learning for Exploring the Variability in Low Height-For-Age From Socioeconomic, Agroecological, and Climate Features in the Northern Province of Rwanda," GeoHealth, vol. 8, 2024, doi: 10.1029/2024GH001027.

S. Rahardiantoro, A. Juhanda, A. Kurnia, A. Aswi, B. Sartono, D. Handayani, A. Soleh, Y. Yanti, and S. Cramb, "Spatio-temporal modeling to identify factors associated with stunting in Indonesia using a Modified Generalized Lasso," Spatial and Spatio-temporal Epidemiology, vol. 51, 2024, p. 100694, doi: 10.1016/j.sste.2024.100694.

E. Ahmed, M. Abonazel, M. Al-Ghamdi, H. Elshamy, and I. Khattab, "Proposed robust estimators for the Poisson panel regression model: application to COVID-19 deaths in Europe," Communications in Mathematical Biology and Neuroscience, 2024, doi: 10.28919/cmbn/8795.

E. Ahmed, M. Abonazel, M. Al-Ghamdi, H. Elshamy, and I. Khattab, "Proposed robust estimators for the Poisson panel regression model: application to COVID-19 deaths in Europe," Communications in Mathematical Biology and Neuroscience, 2024, doi: 10.28919/cmbn/8795.

Quarta, Y., Mondiana, Pramoedyo, H., & Iriany, A. (2024). Applied fixed effect of Geographically Weighted Panel Regression (GWPR) with M- Estimator approach to estimate sugarcane yield data in East Java. Journal of Applied and Natural Science. doi: 10.31018/jans.v16i2.5443.

T. Fayose, K. Ayinde, O. Alabi, and A. Bello, "Robust weighted ridge regression based on S-estimator," African Scientific Reports, 2023, doi: 10.46481/asr.2023.2.3.126.

K. Khotimah, K. Sadik, and A. Kurnia, "The MM estimator method for robust regression," Journal of Physics: Conference Series, vol. 1863, no. 1, p. 012033, 2020, doi: 10.1088/1742-6596/1863/1/012033.

C. Garcia, R. Gómez, and C. García, "Choice of the ridge factor from the correlation matrix determinant," Journal of Statistical Computation and Simulation, vol. 89, pp. 211–231, 2018, doi: 10.1080/00949655.2018.1543423.

D. Wirwana, M. N. Aidi, and A. Fitrianto, "Analysis of factors affecting district/city GRDP in Kalimantan Island," International Journal of Science: Basic and Applied Research (IJSBAR), vol. 64, no. 1, pp. 131-148, 2022.

M. N. Aidi, F. Ernawati, E. Efriwati, N. Nurjanah, R. Rachmawati, E. D. Julianti, D. Sundari, F. Retiaty, A. Fitrianto, K. Nurfadilah, and A. Y. Arifin, "Spatial distribution and identifying biochemical factors affecting hemoglobin levels among women of reproductive age for each province in Indonesia: A geospatial analysis," Geospatial Health, vol. 17, no. 2, p. 1118, 2022, doi: 10.4081/gh.2022.1118.

J. A. Padrón Hidalgo, A. Pérez-Suay, F. Nar, and G. Camps-Valls, "Nonlinear Cook Distance for Anomalous Change Detection," arXiv preprint arXiv:2012.12307, 2020.

J. Zhang, Y. Yang, and J. Ding, "Information criteria for model selection," Wiley Interdisciplinary Reviews: Computational Statistics, vol. 15, 2023, doi: 10.1002/wics.1607.

A. Ghosh and N. Varadharajan, "The association between neighborhood characteristics and depression: was the regression model satisfactory?" The British Journal of Psychiatry, vol. 216, p. 235, 2020, doi: 10.1192/bjp.2020.19.

S. Sifriyani, I. Mandang, F. Amijaya, and R. Ruslan, "Developing Geographically Weighted Panel Regression Model for Spatio-Temporal Analysis of COVID-19 Positive Cases in Kalimantan, Indonesia," Journal of Southwest Jiaotong University, vol. 57, no. 3, 2022, doi: 10.35741/issn.0258-2724.57.3.10.

D. Khan, M. Ali, Z. Ahmad, S. Manzoor, and S. Hussain, "A new efficient redescending M-estimator for robust fitting of linear regression models in the presence of outliers," Mathematical Problems in Engineering, 2021, doi: 10.1155/2021/3090537.

A. Prahutama and A. Rusgiyono, "Robust regression with MM-estimator for modeling the number of maternal mortality of pregnancy in Central Java, Indonesia," Journal of Physics: Conference Series, vol. 1943, 2021, doi: 10.1088/1742-6596/1943/1/012148.

M. Solis-Soto, D. Paudel, and F. Nicoli, "Relationship between vaccination and nutritional status in children: Analysis of recent Demographic and Health Surveys," Demographic Research, vol. 42, 2020, doi: 10.4054/demres.2020.42.1.

C. Wright, F. Petermann-Rocha, R. Bland, P. Ashorn, S. Zaman, and F. Ho, "Weight velocity in addition to latest weight does not improve the identification of wasting or the prediction of stunting and mortality: A longitudinal analysis using data from Malawi, South Africa, and Pakistan," The Journal of Nutrition, vol. 154, pp. 2583-2589, 2024, doi: 10.1016/j.tjnut.2024.06.011.

Himmah, E. F., Kaestria, R., & Riana. (2025). Mathematical modeling of stunting with the influence of nutritional intervention. JTAM (Jurnal Teori dan Aplikasi Matematika), 9(1), 54–67. https://doi.org/10.31764/jtam.v9i1.26817

V. Sutriana, M. Sitaresmi, and A. Wahab, "Risk factors for childhood pneumonia: A case-control study in a high prevalence area in Indonesia," Clinical and Experimental Pediatrics, vol. 64, pp. 588-595, 2021, doi: 10.3345/cep.2020.00339.

H. Wu, C. Yang, and B. Xi, "Association of parental height with offspring stunting in 14 low- and middle-income countries," Frontiers in Nutrition, vol. 8, 2021, doi: 10.3389/fnut.2021.650976.

F. Ernawati, Y. Octaria, and Y. Widodo, "Economic Status, Stunting, and Diet Quality as Important Determinants of Anaemia among Indonesian Children aged 6-35 Months Old: A SEANUTS Study," Malaysian Journal of Medicine and Health Sciences, vol. 16, Supp 13, 2020, doi: 10.2636/2636-9346.




DOI: http://dx.doi.org/10.30829/zero.v9i1.24064

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Publisher :
Department of Mathematics
Faculty of Science and Technology
Universitas Islam Negeri Sumatera Utara Medan
📱 WhatsApp:085270009767 (Admin Official)
SINTA 2 Google Scholar CrossRef Garuda DOAJ