Geographically weighted bivariate generalized Poisson regression: application to infant and maternal mortality data

Purhadi (), Sutikno (), Sarni Maniar Berliana () and Dewi Indra Setiawan ()
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Purhadi: Institut Teknologi Sepuluh Nopember
Sutikno: Institut Teknologi Sepuluh Nopember
Sarni Maniar Berliana: Institut Teknologi Sepuluh Nopember
Dewi Indra Setiawan: Institut Teknologi Sepuluh Nopember

Letters in Spatial and Resource Sciences, 2021, vol. 14, issue 1, No 6, 79-99

Abstract: Abstract Bivariate generalized Poisson regression (BGPR) is an extension of bivariate Poisson regression which deals overdipersion or underdispersion problem. This model gives global regression coefficients for all observations (locations) in the analysis. The BGPR model is then extended to take into account spatial heterogeneity, called geographically weighted bivariate generalized Poisson regression model, that yields varying regression coefficients locally. The regression model is applied to analyse factors affecting number of infant and maternal mortality in East Java, Indonesia.

Keywords: Count data; Spatial analysis; Spatial heterogeneity (search for similar items in EconPapers)
JEL-codes: C10 C18 C31 I10 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12076-021-00266-5

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