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

Sarni Maniar Berliana; Dewi Indra Setiawan

doi:10.1007/s12076-021-00266-5

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

Purhadi
, Sutikno
, Sarni Maniar Berliana^*
, Dewi Indra Setiawan

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

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.

Original language	English
Pages (from-to)	79-99
Number of pages	21
Journal	Letters in Spatial and Resource Sciences
Volume	14
Issue number	1
DOIs	https://doi.org/10.1007/s12076-021-00266-5
Publication status	Published - Apr 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Count data
Spatial analysis
Spatial heterogeneity

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10.1007/s12076-021-00266-5

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title = "Geographically weighted bivariate generalized Poisson regression: application to infant and maternal mortality data",

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",

author = "Purhadi and Sutikno and Berliana, \{Sarni Maniar\} and Setiawan, \{Dewi Indra\}",

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AU - Purhadi,

AU - Sutikno,

AU - Berliana, Sarni Maniar

AU - Setiawan, Dewi Indra

PY - 2021/4

Y1 - 2021/4

N2 - 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.

AB - 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.

KW - Count data

KW - Spatial analysis

KW - Spatial heterogeneity

UR - https://www.scopus.com/pages/publications/85104214842

U2 - 10.1007/s12076-021-00266-5

DO - 10.1007/s12076-021-00266-5

M3 - Article

AN - SCOPUS:85104214842

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JO - Letters in Spatial and Resource Sciences

JF - Letters in Spatial and Resource Sciences

IS - 1

ER -

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

Abstract

UN SDGs

Keywords

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