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All Journal Xplore: Journal of Statistics
Khusnul Khotimah
Departemen Statistika, IPB University
Decision Sciences, Operations Research & Management Engineering Mathematics

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Pemodelan dengan Geographically Weighted Negative Binomial Regression (Studi kasus: Banyaknya Penderita Kusta di Jawa Barat) Khusnul Khotimah; Itasia Dina Sulvianti; Pika Silvianti
Xplore: Journal of Statistics Vol. 10 No. 3 (2021)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (805.227 KB) | DOI: 10.29244/xplore.v10i3.833

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.
Co-Authors Itasia Dina Sulvianti Pika Silvianti