Pemodelan Regresi Binomial Negatif Bivariat pada Data Jumlah Kematian Ibu dan Bayi di Provinsi Sulawesi Selatan Tahun 2020

Article History

Submited : January 17, 2023
Published : February 14, 2023

In general, negative binomial regression is used for univariate discrete data that is overdispersive and follows the Poisson distribution. In the real world, a case is often influenced by two discrete variables that are correlated with each other. Therefore, in this paper we will examine the regression that is influenced by two independent variables, has overdispersion properties and follows a bivariate Poisson distribution. This regression is called bivariate negative binomial regression with model parameters estimated using the Maximum Likelihood Estimation (MLE) method and Newton Raphson iterations. The formation of this model is based on the Famoye method, while in general it uses the Cheon method. Furthermore, the results of this study were applied to data on the number of maternal and infant deaths in South Sulawesi Province in 2020. The results obtained were the number of puskesmas that had a significant effect on the number of maternal deaths and the proportion of handling obstetric complications, the proportion of pregnant women implementing the K4 program, the proportion of deliveries in facilities health services, the proportion of postpartum mothers implementing the KF2 program and the number of puskesmas have a significant effect on the number of infant deaths.

References

  1. Sekarmini, N. M., Sukarsa I. K. G., & Srinadi, I. G. A. M. Penerapan Regresi Zero Inflated Negative Binomial (ZINB) untuk Pendugaan Kematian Anak Balita. E-Jurnal Matematika, 2(4), 2013.
  2. Harris, T., Yang, Z., & Hardin, J. W. Modeling underdispersed count data with generalized Poisson regression. The Stata Journal, 12(4), 2012.
  3. Chou, N. T., & Steenhard, D. Bivariate Count Data Regression Models. Statistics and Data Analysis, Paper, 2011.
  4. Cameron, A. C., & Johansson, P. Bivariate count data regression using series expansions: with applications (No. 98-15). Working Paper, 1998.
  5. Fitriyanti, W., & Kurniawan, U. Regresi Negatif Binomial Bivariat untuk Mengatasi Overdispersi Regresi Poisson Bivariat. Jurnal Statistika Universitas Muhammadiyah Semarang, 7(1), 2019.
  6. Cheon, S., Song, S. H., & Jung, B. C. Tests for Independence in a Bivariate Negative Binomial Model. Journal of the Korean Statistical Society, 38(2), 2009.
  7. Famoye, F. On The Bivariate Negative Binomial Regression Model. Journal of Applied Statistics, 37(6), 2010.
  8. Rahmadeni, R., & Jannah, F. F. Pemodelan Generalized Poisson Regression (GPR) pada Kasus Kematian Neonatal di Provinsi Riau. Jurnal Sains Matematika dan Statistika, 5(2), 2019.
  9. Hogg, R. V., & Craig, A. T. (1995). Introduction to Mathematical Statistics. (Sixth Edition). Englewood Hills, New Jersey.
  10. Rahayu, A. Model-Model Regresi untuk Mengatasi Masalah Overdipersi pada Regresi Poisson. Journal Peqguruang: Conference Series, 2(1), 2020.
  11. Keswari, N. M. R., Sumarjaya, I. W., & Suciptawati, N. L. P. Perbandingan Regresi Binomial Negatif dan Regresi Generalisasi Poisson dalam Mengatasi Overdispersi. E-Jurnal Matematika, 3(3), 2014.
  12. Hilbe, J. M. (2011). Negative Binomial Regression. Cambridge University Press.
  13. Sauddin, A., Auliah, N. I., & Alwi, W. Pemodelan Jumlah Kematian Ibu di Provinsi Sulawesi Selatan Menggunakan Regresi Binomial Negatif. Jurnal MSA (Matematika dan Statistika serta Aplikasinya), 8(2), 2020.
  14. Hariadi, W., & Sulantari, S. Estimasi Model Regresi Binomial Negatif Bivariat (BNBR) pada Penderita Kusta di Jawa Timur. Unisda Journal of Mathematics and Computer Science (UJMC), 5(2), 2019.
  15. Dinas Kesehatan Provinsi Sulawesi Selatan. 2020. Profil Dinas Kesehatan Provinsi Sulawesi Selatan Tahun 2020.

Downloads

Download data is not yet available.
Fulltext
statcounter