TY - JOUR
T1 - Geographically Weighted Bivariate Gamma Regression in the Analysis of Maternal Mortality Rate and Infant Mortality Rate in North Sumatra Province 2017
AU - Dewi, Diah Kusuma
AU - Purhadi,
AU - Sutikno,
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?scp=85069495091&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/546/5/052020
DO - 10.1088/1757-899X/546/5/052020
M3 - Conference article
AN - SCOPUS:85069495091
SN - 1757-8981
VL - 546
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
IS - 5
M1 - 052020
T2 - 9th Annual Basic Science International Conference 2019, BaSIC 2019
Y2 - 20 March 2019 through 21 March 2019
ER -