Geographically Weighted Negative Binomial Regression (GWNBR) Modeling In Infant Mortality Rate Cases In South Sulawesi
DOI:
https://doi.org/10.36456/jstat.vol18.no1.a10093Keywords:
GWNBR, Adaptive Tricube Kernel, Infant MortalityAbstract
Geographically Weighted Negative Binomial Regression (GWNBR) is a method used to model count data that exhibit overdispersion and spatial heterogeneity. South Sulawesi is one of the provinces experiencing an increase in infant mortality cases. Therefore, this study aims to obtain a better model for mapping the factors that influence infant mortality cases in South Sulawesi Province. The method used in this study is GWNBR with an Adaptive Tricube Kernel as the weighting function. The results show that the GWNBR model with Adaptive Tricube Kernel weighting produces the smallest AIC value, which is 223.4447, making it more effective for modeling infant mortality cases in South Sulawesi Province. The variables significantly affecting infant mortality cases include X1 (Percentage of Exclusive Breastfeeding), X2 (Percentage of Early Initiation of Breastfeeding), X3 (Complete Baby Visit Coverage), X4 (Percentage of Vitamin A Supplementation), X5 (Number of Community Health Centers), X6 (Percentage of Low Birth Weight Babies), X7 (Delivery Coverage in Health Service Facilities), and X8 (Iron Tablet Supplementation to Pregnant Women).
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