Comparison of Bayesian Spatial Leroux CAR Models with Poisson and Binomial Likelihoods for Modeling Stunting Cases - Rika Saniarti; Aswi Aswi (Journal of Mathematics, Computations and Statistics, 2026)
Stunting remains a chronic nutritional problem requiring precise spatial mapping to support effective policy interventions in East Java, Indonesia. Spatial disease mapping commonly applies the Poisson distribution with Conditional Autoregressive (CAR) effects; however, the Poisson distribution is sensitive to overdispersion. Alternatively, the Bayesian Spatial CAR model with a Binomial likelihood may offer a better framework, yet empirical comparisons remain limited. This study compares the performance of Bayesian Spatial Leroux CAR models with Poisson and Binomial likelihoods in modeling stunting cases and identifies associated factors. The data include stunting cases across 38 districts in East Java (2024) and predictors: low birth weight (LBW), prematurity, exclusive breastfeeding, complete basic immunization (CBI), pneumonia, and diarrhea. Performance was evaluated using the Deviance Information Criterion (DIC) and the Watanabe–Akaike Information Criterion (WAIC). Results indicate significant spatial dependence. The Bayesian Spatial Leroux CAR model with a binomial likelihood outperforms the Poisson-based model. LBW, exclusive breastfeeding, CBI, pneumonia, and diarrhea are significantly associated with stunting. Kediri Regency exhibits the highest relative risk (RR), followed by Probolinggo Regency and Batu City, while Kediri City and Ponorogo Regency show the lowest RR