Handling of Overdispersion in the Poisson Regression Model with Negative Binomial for the Number of New Cases of Leprosy in Java

Penanganan Overdispersi pada Model Regresi Poisson dengan Binomial Negatif untuk Jumlah Kasus Baru Kusta di Jawa

Authors

  • Yopi Ariesia Ulfa Badan Pusat Statistik, Indonesia
  • Agus M Soleh Department of Statistics, IPB University, Indonesia
  • Bagus Sartono Department of Statistics, IPB University, Indonesia

DOI:

https://doi.org/10.29244/ijsa.v5i1p1-13

Keywords:

leprosy, negative binomial regression, overdispersion, poisson regression

Abstract

Based on data from the Directorate General of Disease Prevention and Control of the Ministry of Health of the Republic of Indonesia, in 2017, new leprosy cases that emerged on Java Island were the highest in Indonesia compared to the number of events on other islands. The purpose of this study is to compare Poisson regression to a negative binomial regression model to be applied to the data on the number of new cases of leprosy and to find out what explanatory variables have a significant effect on the number of new cases of leprosy in Java. This study's results indicate that a negative binomial regression model can overcome the Poisson regression model's overdispersion. Variables that significantly affect the number of new cases of leprosy based on the results of negative binomial regression modeling are total population, percentage of children under five years who had immunized with BCG, and percentage of the population with sustainable access to clean water.

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Published

2021-03-31

How to Cite

Ulfa, Y. A., Soleh, A. M., & Sartono, B. (2021). Handling of Overdispersion in the Poisson Regression Model with Negative Binomial for the Number of New Cases of Leprosy in Java: Penanganan Overdispersi pada Model Regresi Poisson dengan Binomial Negatif untuk Jumlah Kasus Baru Kusta di Jawa. Indonesian Journal of Statistics and Its Applications, 5(1), 1–13. https://doi.org/10.29244/ijsa.v5i1p1-13

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