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Geographically Weighted Bivariate Poisson Regression to Modelling of the Number of HIV and AIDS Cases in Central Java Province

Purhadi, Triyanto

Abstract



Geographically Weighted Bivariate Poisson Regression (GWBPR) is a statistical method on spatial data which used to analyze the relationship between count data of the two response variables and a set of the independent variables. In this paper is studied of parameters estimation and hypothesis testing in GWBPR to the modelling of the number of HIV and AIDS cases in Central Java Province, Indonesia. The problem of parameter estimation, MLE cannot find an analytical solution, so it is applied iterative procedure by the Newton-Raphson algorithm. The result of an empirical study is obtained that the model of GWBPR is better than the BPR to the modelling of the number of HIV and AIDS cases. In the model GWBPR, social problems (unemployment, school drop-out, and divorce) are the variables that most influence to the number of HIV and AIDS cases for each of regency/city in Central Java Province.

Keywords


GWBPR, bivariate Poisson regression, spatial data, HIV, AIDS

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