| dc.description.abstract | Poisson regression has been widely used for modeling counts
data. Violation of equidispersion assumption can occur when there are
excess of zeros of the data. For that condition we can use Zero-Inflated
Poisson (ZIP) to analyze such data, resulting global parameter estimates.
However spatial data from various locations have their own characteristics
depend on their socio-cultural, geographical and economic conditions. In
this paper, we first review the theoretical framework of Zero-Inflated
Poisson (ZIP) and Geographically Weighted Zero Inflated Poisson
(GWZIP) regression. We use Maximum Likelihood (MLE) method and EM
algorithm to estimate the model parameters. The F test is used to compare
the two models. Second, we fit these models to the number of filariasis case
of East Java. In our case, there is the preponderance of zeros in the data set
(65.79%). The results prove that the spatial dependence is absent, but there
is weak spatial heterogeneity of the data (significance level α = 0.1). Based
on F test, ZIP and GWZIP regression are not significantly different. | en_US |
