NON-NESTED SPATIAL COUNT REGRESSION MODEL SELECTION IN HEALTH INSURANCE
In the most cases, Poisson distribution is used for a count regression model. In this paper, not only Poisson Distribution that examined, but also another distribution such as Generalized Poisson, that capable of modeling overdisperion, and Zero- Inflated Generalized Poisson, that capable of mode...
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| Main Author: | |
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| Format: | Final Project |
| Language: | Indonesia |
| Subjects: | |
| Online Access: | https://digilib.itb.ac.id/gdl/view/33875 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | In the most cases, Poisson distribution is used for a count regression model. In this
paper, not only Poisson Distribution that examined, but also another distribution
such as Generalized Poisson, that capable of modeling overdisperion, and Zero-
Inflated Generalized Poisson, that capable of modeling excess zeros in response
distribution. Then I also add spatial effect to the regression model. With the
addition of these spatial effects, Bayesian approached is considered which allows
the modeling for a spatial dependency pattern. The addition of spatial effects of
each location caused the model to be having a lot of parameters. Thus MCMC
algorithm is used to estimate the parameters. Because the models to be compared
come from different distribution models so the models are categorized as a nonnested
models. To compare the models that are non-nested, we use Vuong test and
Clarke test. Provided that the Generalized Poisson distribution is a better
distribution to other models for the data that used in this paper. |
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