Identifikasi Faktor Risiko Kematian Ibu dan Neonatal di Kalimantan Menggunakan Model BPGIGR dengan Algoritma BHHH - Auliarahmi, Annisa; Purhadi; Shofi Andari; Muhammad Fikri; Pitriani (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia, 2025)
Poisson regression is widely applied for modeling count data and requires the strict assumption of equidispersion, meaning that the mean and variance of the data must be equal. In practice, this condition is rarely satisfied. To address this issue, the Bivariate Poisson Generalized Inverse Gaussian Regression (BPGIGR) model was developed by combining the Poisson distribution with the Generalized Inverse Gaussian (GIG) distribution to overcome overdisperion in two correlated response variables. This study aims to obtain parameter estimates and corresponding test statistics for the BPGIGR model by incorporating two exposure variables to account to account for differences in population size across analytical units. Parameter estimation is performed using the Maximum Likelihood Estimation (MLE) method with the Berndt-Hall-Hall-Hausman (BHHH) algorithm. The BPGIGR model is implemented on maternal and neonatal deaths in Kalimantan in 2024 to identify the significant contributing factors. The results indicate that the model is influenced by the percentages of active posyandu, low birth weight, complete neonatal visits, exclusive breasfeeding, K4 visits, and pregnant women receiving iron tablets with an AICc of 9.719,092.