PERBANDINGAN REGRESI ZERO INFLATED POISSON (ZIP) DAN REGRESI ZERO INFLATED NEGATIVE BINOMIAL (ZINB) PADA DATA OVERDISPERSION (Studi Kasus: Angka Kematian Ibu di Provinsi Bali)
ABSTRACT: Poisson regression
is a nonlinear regression which is often used for count data and has equidispersion
assumption (variance value equal to mean value). However in practice,
equidispersion assumption is often violated. One of it violations is
overdispersion (variance value greater than the mean value). One of the causes
of overdipersion is excessive number of zero values on the response variable
(excess zeros). There are many methods to handle overdispersion because of
excess zeros. Two of them are Zero Inflated Poisson (ZIP) regression and Zero
Inflated Negative Binomial (ZINB) regression. The purpose of this research is
to determine which regression models is better in handling overdispersion data.
The data that can be analyzed using the ZIP and ZINB regression is maternal mortality
rate in the Province of Bali. Maternal mortality rate data has proportion of
zeros value more than 50% on the response variable. In this research, ZINB
regression better than ZIP regression for modeling maternal mortality rate. The
independent variable that affects the number of maternal mortality rate in the
Province of Bali is the percentage of mothers who carry a pregnancy visit, with
ZINB regression models and
Keywords: Overdispersion,
Poisson Regression, ZIP Regression, ZINB Regression, Maternal Mortality Rate in
the Province of Bali
Ni Putu Prema Dewanti, Made Susilawati
Kod Jurnal: jpmatematikadd160171
