Pemodelan dengan Geographically Weighted Negative Binomial Regression (Studi kasus: Banyaknya Penderita Kusta di Jawa Barat)

Authors

  • Khusnul Khotimah Departemen Statistika, IPB University
  • Itasia Dina Sulvianti Departemen Statistika, IPB University
  • Pika Silvianti Departemen Statistika, IPB University

DOI:

https://doi.org/10.29244/xplore.v10i3.833

Keywords:

leper, negative binomial regression, Poisson regression, overdispersion, geographically weighted negative binomial regression

Abstract

The number of leper in West Java is an example of the count data case. The analyzes commonly used in count data is Poisson regression. This research will determine the variables that influence the number of leper in West Java. The data used is the number of leper in West Java in 2019. This data has an overdispersion condition and spatial heterogenity. To handle overdispersion, the negative binomial regression model can be employed. While spatial heterogenity is overcome by adding adaptive bisquare kernel weight. This research resulted Geographically Weighted Negative Binomial Regression (GWNBR) with a weighting adaptive bisquare kernel classifies regency/city in West Java into ten groups based on the variables that sigfinicantly influence the number of leper. In general, the variable in the percentage of households with Clean and Healthy Behavior (PHBS) has a significant effect in all regency/city in West Java. Especially for Bogor Regency, Depok City, Bogor City, and Pangandaran Regency, the variable of the percentage of people poverty does not have a significant effect on the number leper.

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Published

2021-09-30

How to Cite

Khotimah, K., Sulvianti, I. D. ., & Silvianti, P. (2021). Pemodelan dengan Geographically Weighted Negative Binomial Regression (Studi kasus: Banyaknya Penderita Kusta di Jawa Barat). Xplore: Journal of Statistics, 10(3), 227–237. https://doi.org/10.29244/xplore.v10i3.833

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