DOI: https://doi.org/10.14710/j.gauss.10.4.488-498

PERBANDINGAN MODEL REGRESI BINOMIAL NEGATIF BIVARIAT DENGAN MODEL GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL BIVARIAT REGRESSION (GWNBBR) PADA KASUS ANGKA KEMATIAN BAYI DAN KEMATIAN IBU DI JAWA TENGAH

*Yashmine Noor Islami  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Dwi Ispriyanti  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Puspita Kartikasari  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2021 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract

Infant mortality (0-11 months) and maternal mortality (during pregnancy, childbirth, and postpartum) are significant indicators in determining the level of public health. Central Java Province which has 35 regencies/cities is included in the top five regions with the highest number of infant and maternal mortality in Indonesia. The data characteristics of the number of infants and maternal mortality are count data. Therefore, the Poisson Regression method can be used to analyze the factors that influence the number of infants and maternal mortality. In Poisson regression analysis, there must be a fulfilled assumption, called equidispersion. Frequently, the variance of count data is greater than the mean, which is known as the overdispersion. The research, binomial negative bivariate regression is used as a solutions to overcome the problem of overdispersion in poisson regression. This method produce a global model. In reality, the geographical, socio-cultural, and economic conditions of each region will be different. This illustrates the effect of spatial heterogeneity, so it needs to be developed into Geographically Weighted Negative Binomial Bivariate Regression (GWNBBR). The model of GWNBBR provides weighting based on the position or distance from one observation area to another. Significant variables for modeling infant mortality cases included the percentage of obstetric complications treated (X1), the percentage of infants who were exclusively breastfed (X3), and the percentage of poor people (X5). Significant variable for modeling maternal mortality cases is the percentage of poor people (X5). Based on the AIC value, GWNBBR model is better than binomial negatif bivariat regression model because it has a smaller AIC value.

 

Fulltext View|Download
Keywords: Infant Mortality, Maternal mortality, Overdispersion, Negative Binomial Bivariate Regression, GWNBBR

Article Metrics:

Article Info
Section: Articles
Language : ID
Recent articles
PEMODELAN DAN PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN ARIMAX-TARCH PENDEKATAN METODE MARKOWITZ UNTUK OPTIMALISASI PORTOFOLIO DENGAN RISIKO EXPECTED SHORTFALL (ES) PADA SAHAM SYARIAH DILENGKAPI GUI MATLAB PERBANDINGAN MODEL REGRESI BINOMIAL NEGATIF BIVARIAT DENGAN MODEL GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL BIVARIAT REGRESSION (GWNBBR) PADA KASUS ANGKA KEMATIAN BAYI DAN KEMATIAN IBU DI JAWA TENGAH More recent articles
  1. Best, D. J. 1999. Tests of fit and other nonparametric data analysis. Thesis. University of Wollongong
  2. Bozdogan, H. 2000. Akaike’s Information Criterion and Recent Developments in Information Complexity. Journal of Mathematical Psychology Vol. 44, No. 1: Hal. 62–91
  3. Cameron, A.C., Trivedi, P. K. 1998. Regression Analysis of Count Data. New York: Cambridge University Press
  4. Chasco, C., García, I., Vicéns, J. 2008. Modeling Spatial Variations in Household Disposable Income with Geographically Weighted Regression. Munich Personal RePEc Archive Paper No. 9581
  5. [Dinkes Jateng] Dinas Kesehatan Provinsi Jawa Tengah. 2019. Profil Kesehatan Provinsi Jawa Tengah Tahun 2018. Semarang: Dinas Kesehatan Provinsi Jawa Tengah
  6. Draper, N. R., Smith, H. 1998. Applied Regression Analysis Third Edition. Canada: John Wiley and sons, Inc
  7. Famoye, F. 2010. On The Bivariate Negative Binomial Regression Model. Journal of Applied Statistics Vol. 37, No. 6: Hal. 969–981
  8. Fitriyanti, W., Kurniawan, U. 2019. Regresi Negatif Binomial Bivariat untuk Mengatasi Overdispersi Regresi Poisson Bivariat. Statistika Vol. 7, No. 1: Hal. 39–46
  9. Fotheringham, A.S. Brundson, C. dan Charlton, M. 2002. Geographically Weighted Regression. UK: John Wiley and Sons, Chichester
  10. Gujarati, D. N. 2003. Basic Econometrics Fourth Edition. New York: The McGraw Hill Companies
  11. Hilbe, J. M. 2011. Negative Binomial Regression Second Edition. New York: Cambridge University Press
  12. Johnson, R. A., Wichern, D. W. 2007. Applied Multivariate Statistical Analysis. USA: Pearson Practice Hall
  13. Karlis, D., Ntzoufras, I. 2005. Bivariate Poisson and Diagonal Inflated Bivariate Poisson Regression Models in R. Journal of Statistical Software Vol. 14, No. 10:Hal. 1–36
  14. [Kemenkes RI] Kementerian Kesehatan Republik Indonesia. 2020. Profil Kesehatan Indonesia Tahun 2019. Jakarta: Kementerian Kesehatan Republik Indonesia
  15. Long, J. S. 1997. Regression Models for Categorical and Limited Dependent Variables. California: Sage Publications
  16. McClave, J. T., Benson, P.G., Sincich, T. 2018. Statistics for Business and Economics Thirteenth Edition. United Kingdom: Pearson Education Inc
  17. Prahutama, A., Sudarno, Suparti, Mukid, M. A. 2017. Analisis Faktor-Faktor yang Mempengaruhi Angka Kematian Bayi di Jawa Tengah Menggunakan Regresi Generalized Poisson dan Binomial Negatif. Statistika Vol. 5, No. 2: Hal. 1–6
  18. Ricardo, A. & Carvalho, T. 2014. Geographically Weighted Negative Binomial Regression—Incorporating Overdispersion. Springer Vol. 24: Hal. 769–783

Last update:

No citation recorded.

Last update:

No citation recorded.