Geographically weighted bivariate zero inflated generalized Poisson regression model and its application

Purhadi; Dewi Novita Sari; Qurotul Aini; Irhamah

doi:10.1016/j.heliyon.2021.e07491

Geographically weighted bivariate zero inflated generalized Poisson regression model and its application

Heliyon. 2021 Jul 8;7(7):e07491. doi: 10.1016/j.heliyon.2021.e07491. eCollection 2021 Jul.

Authors

Purhadi¹, Dewi Novita Sari^{1

2}, Qurotul Aini^{1

2}, Irhamah¹

Affiliations

¹ Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia.
² BPS-Statistics Indonesia, Jl. Dr. Sutomo 6-8, Jakarta 10710, Indonesia.

Abstract

This study discusses the development of Zero Inflated Generalized Poisson Regression (ZIGPR) with two response variables, that is Bivariate ZIGPR (BZIGPR). The extension of the ZIGPR model by considering spatial factor called Geographically Weighted Zero Inflated Generalized Poisson Regression (GWBZIGPR). The GWBZIGPR produces a local parameter estimator for each location of observation. The parameter estimation using the Maximum Likelihood Estimation (MLE) method obtained an equation that did not closed-form so that the numerical iteration of Berndt Hall Hall Hausman (BHHH) is used. The data used in this study are the number of pregnant maternal mortality and postpartum maternal mortality data in 91 sub-districts in Pekalongan Residency, Central Java Province. The results showed that the Akaike Information Criterion Corrected (AICc) value in the GWBZIGPR model is smaller than BZIGPR, so it means that the GWBZIGPR is better than the BZIGPR for modeling the number of pregnant maternal mortality and postpartum maternal mortality in Pekalongan Residency. The results of this study will assist local governments in anticipating the causes of maternal mortality.

Keywords: GWBZIGPR; MLE; MLRT; Maternal mortality rate.