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GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION UNTUK MENANGANI OVERDISPERSI PADA JUMLAH PENDUDUK MISKIN

*Nova Delvia  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Mustafid Mustafid  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Hasbi Yasin  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2021 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

Poverty is a condition that is often associated with needs, difficulties an deficiencies in various life circumstances. The number of poor people in Indonesia increase in 2020. This research focus on modelling the number of poor people in Indonesia using Geographically Weighted Negative Binomial Regression (GWNBR) method. The number of poor people is count data, so analysis used to model the count data is poisson regression.  If there is overdispersion, it can be overcome using negative binomial regression. Meanwhile to see the spatial effect, we can use the Geographically Weighted Negative Binomial Regression method. GWNBR uses a adaptive bisquare kernel for weighting function. GWNBR is better at modelling the number of poor people because it has the smallest AIC value than poisson regression and negative binomial regression. While the GWNBR method obtained 13 groups of province based on significant variables.    

 

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Keywords: GWNBR, Poisson regression, Spatial Effect, Overdispersion, Poverty

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  1. Ardiyanti, S. T., & Purhadi. (2010). Pemodelan Angka Kematian Bayi dengan Pendekatan Geographically Weighted Poisson Regression (GWPR) di Provinsi Jawa Timur. Undergraduate Theses
  2. Brundson, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically Weighted Regression: A Method for Exploring Spatial Nonstationary. Geographical Analysis. 28 (4) , 281 - 298
  3. Hardin, J. W., & Hilbe, J. M. (2007). Generalized Linear Models and Extensions Second Edition. Texas: Stata Press
  4. Hocking, R. R. (1996). Methods and Applications of Linear Models : Regression and The Analysis of Variance. New York: John Wiley and Sons
  5. McCullagh, P., & Nelder, J. A. (1989). Generalized Linear Models. London: Chapman and Hall
  6. Ricardo, A., & Carvalho, T. (2013). Geographically Weighted Negative Binomial Regression-Incorporating Overdispersion. Business Media New York: Springer Science
  7. Sajogyo, T. (1997). Garis Kemiskinan dan Kebutuhan Minimum Pangan. Bogor: LPSBIPB
  8. WorldBank. (1990). World Development Report 1990 : Poverty. New York: Oxford University Press

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