Estimation of Stunting and Wasting in Sumatra 2022 with Nadaraya-Watson Kernel and Penalized Spline - Cinta Rizki Oktarina; Sigit Nugroho; Idhia Sriliana; Pepi Novianti; Etis Sunandi; Reza Pahlepi (Inferensi, 2025)
This study aims to estimate the prevalence of Stunting and Wasting in Sumatra in 2022 using nonparametric regression methods, specifically the Nadaraya-Watson Kernel and Penalized Spline regression models. Both models were applied to assess the relationship between these two correlated response variables and various predictor variables, such as low birth weight, sanitary facilities, poor population, and exclusive breastfeeding. The results showed that the Nadaraya-Watson Kernel regression, particularly using the Gaussian kernel, provided the best fit with minimal prediction error, as indicated by its low Generalized Cross-Validation (GCV) value of 0.024 and high R-squared values (0.9992 for Stunting and 0.9995 for Wasting). In contrast, the Epanechnikov kernel and Biweight kernel produced higher GCV values (0.110 and 0.356, respectively), indicating less optimal performance. For the Penalized Spline model, optimal parameters were determined with a smoothing parameter λ of 5 and 3 knots, which balanced model flexibility and smoothness. This research underscores the potential of nonparametric regression techniques in capturing complex relationships in health data and provides insights for improving interventions aimed at addressing child malnutrition in Indonesia.