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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

  • Diah Kusuma Dewi*
  • , Purhadi
  • , Sutikno
  • *Corresponding author for this work
  • Institut Teknologi Sepuluh Nopember

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)

Abstract

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).

Original languageEnglish
Article number052020
JournalIOP Conference Series: Materials Science and Engineering
Volume546
Issue number5
DOIs
Publication statusPublished - 1 Jul 2019
Event9th Annual Basic Science International Conference 2019, BaSIC 2019 - Malang, Indonesia
Duration: 20 Mar 201921 Mar 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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