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地理加权双变量零膨胀广义泊松回归模型及其应用。

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

作者信息

Sari Dewi Novita, Aini Qurotul

机构信息

Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia.

BPS-Statistics Indonesia, Jl. Dr. Sutomo 6-8, Jakarta 10710, Indonesia.

出版信息

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

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.

摘要

本研究讨论了具有两个响应变量的零膨胀广义泊松回归(ZIGPR)的发展,即二元ZIGPR(BZIGPR)。通过考虑空间因素对ZIGPR模型进行扩展,称为地理加权零膨胀广义泊松回归(GWBZIGPR)。GWBZIGPR为每个观测位置生成一个局部参数估计量。使用最大似然估计(MLE)方法进行参数估计得到了一个没有闭式解的方程,因此使用了伯恩特·霍尔·霍尔·豪斯曼(BHHH)数值迭代法。本研究使用的数据是中爪哇省北加浪岸摄政区91个分区的孕产妇死亡率和产后孕产妇死亡率数据。结果表明,GWBZIGPR模型中的校正赤池信息准则(AICc)值小于BZIGPR,这意味着在对北加浪岸摄政区的孕产妇死亡率和产后孕产妇死亡率进行建模时,GWBZIGPR比BZIGPR更好。本研究结果将有助于地方政府预测孕产妇死亡原因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ee/8319482/48b3de3c3c6f/gr001.jpg

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