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Geographically Weighted Bivariate Gamma Regression in The Analysis of Maternal Mortality Rate and Infant Mortality Rate in North Sumatra Province 2017

机译:2017年北苏门答腊省孕产妇死亡率和婴幼儿死亡率分析的地理加权生物伽玛回归

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In this study, Geographically Weighted Bivariate Gamma Regression(GWBGR)model is proposed.This GWBGR model is a developer of the Bivariate Gamma Regression(BGR)model which all of the regression parameters depend on the geographical location, i.e latitude, and longitude.In these models, the response variables are correlated and follow the gamma distribution.We applied the GWBGR model to analyze Maternal Mortality Rate(MMR)and Infant Mortality Rate(IMR)in North Sumatra Province 2017.The result shows that the test of heterogeneity spatial is significant, it means MMR and IMR in North Sumatra Province depend on the geographical location.Modelling with BGR produced 6 groups based on significant variable similarities to MMR and 3 groups based on significant similarity of variables towards IMR.Based on AICc, GWBGR model is smallest than BGR model.Finally, we conclude that the GWBGR model was better than the BGR Model(global model).
机译:在这项研究中,提出了地理加权的双相伽玛回归(GWBGR)模型。这是GWBGR模型的一种开发人员,其BGRIATIEN回归(BGR)模型,所有回归参数取决于地理位置,即纬度和经度。这些模型,响应变量是相关的,遵循伽玛分布。我们应用GWBGR模型分析了北苏门答腊省2017年的母体死亡率(MMR)和婴儿死亡率(IMR)。结果表明异质性空间的试验是它意味着北苏门答腊省MMR和IMR依赖于地理位置。基于与MMR和3组的显着变量与IMR的显着相似性,基于MMR和3组的MMR和3组向BGR产生了6组。基于AICC,GWBGR模型最小比BGR模型。最后,我们得出结论,GWBGR模型比BGR模型更好(全球模型)。

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