• Untung Kurniawan Badan Pusat Statistik (BPS) Kabupaten Klaten, Indonesia
Keywords: bivariate poisson regression, infant mortality, maternal mortality, maximum likelihood estimation


Poisson regression is a regression model which often used to analyze the count data. In this study, poisson regression has been used bivariate poisson regression where the regression is a method which is used to model a pair of correlated count data with multiple predictor variables. The model is used covariance which has a function of the independent variable. The purposes of this study is obtain parameter estimates, test statistics of bivariate poisson regression, and determine the factors that influence of infant mortality and maternal mortality. The data is used from the infant mortality and maternal mortality in Central Java 2015. Based on the result, the parameter estimation of poisson bivariate regression model using maximum likelihood (MLE) method. The results obtained from the parameter estimation are not close form so it needs to be done by Newton-Raphson iteration method. In testing the hypothesis using the Maximum Likelihood Ratio Test method (MLRT) by comparing the value between likelihood below H0 and likelihood below population. Partial of parameters model λ1 (infant mortality) there are six independent variables that have significant influence, namely, delivery by health personnel (X1), pregnant women carry out the program K4 (X3), pregnant women who get Fe3 tablet (X4), handling obstetric complication (X5), exclusively breastfed infants (X7), and households living a clean and healthy life (X8). While for model λ2 (maternal death) only variable handling of neonatal complication (X6) which have no significant influence to response variable.


Cameron, A.C. dan Trivedi, P.K. (1998), Regression Analysis of Count Data, Cambridge University Press, USA.
Chou, N. dan Steenhard, D. (2011), “Bivariate Count Data Regression Models – A SAS® Macro Program”, Proceedings SAS Global Forum 2011, paper 355-2011.
[Dinkes] Dinas Kesehatan Propinsi Jawa Tengah. (2015). Profil Kesehatan Propinsi Jawa Tengah. Semarang : Dinkes Jateng.
Gurmu, S. dan Elder, J. (2007) "A simple bivariate count data regression model." Economics Bulletin, Vol.3, No. 11, hal. 1-10.
Karlis, D. dan Ntzoufras, I. (2005). Bivariate Poisson and Diagonal Inflated Bivariate Poisson Regression Models in R. Journal of Statistical Software, Vol 14, 1-36.
Long, J. S. (1997), Regression Models for Categorical and Limited Dependent Variables. Number 7 in Advance Quantitive Techniques in The Social Sciences, Sage Publications, California.