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

Author

Listed:
  • Purhadi

    (Institut Teknologi Sepuluh Nopember)

  • Sutikno

    (Institut Teknologi Sepuluh Nopember)

  • Sarni Maniar Berliana

    (Institut Teknologi Sepuluh Nopember
    Politeknik Statistika STIS)

  • Dewi Indra Setiawan

    (Institut Teknologi Sepuluh Nopember)

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.

Suggested Citation

  • Purhadi & Sutikno & Sarni Maniar Berliana & Dewi Indra Setiawan, 2021. "Geographically weighted bivariate generalized Poisson regression: application to infant and maternal mortality data," Letters in Spatial and Resource Sciences, Springer, vol. 14(1), pages 79-99, April.
  • Handle: RePEc:spr:lsprsc:v:14:y:2021:i:1:d:10.1007_s12076-021-00266-5
    DOI: 10.1007/s12076-021-00266-5
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    References listed on IDEAS

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    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273.
    2. Hossein Zamani & Pouya Faroughi & Noriszura Ismail, 2016. "Bivariate generalized Poisson regression model: applications on health care data," Empirical Economics, Springer, vol. 51(4), pages 1607-1621, December.
    3. Vernic, Raluca, 1997. "On The Bivariate Generalized Poisson Distribution," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 23-32, May.
    4. A. Stewart Fotheringham & Taylor M. Oshan, 2016. "Geographically weighted regression and multicollinearity: dispelling the myth," Journal of Geographical Systems, Springer, vol. 18(4), pages 303-329, October.
    5. Steven Farber & Antonio Páez, 2007. "A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations," Journal of Geographical Systems, Springer, vol. 9(4), pages 371-396, December.
    6. Karlis, Dimitris & Ntzoufras, Ioannis, 2005. "Bivariate Poisson and Diagonal Inflated Bivariate Poisson Regression Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i10).
    7. Felix Famoye & P. Consul, 1995. "Bivariate generalized Poisson distribution with some applications," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 42(1), pages 127-138, December.
    8. Felix Famoye, 2015. "A Multivariate Generalized Poisson Regression Model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(3), pages 497-511, February.
    9. Weiren Wang & Felix Famoye, 1997. "Modeling household fertility decisions with generalized Poisson regression," Journal of Population Economics, Springer;European Society for Population Economics, vol. 10(3), pages 273-283.
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    Cited by:

    1. Alexandru Bănică & Ionel Muntele, 2023. "Local and regional factors of spatial differentiation of the excess mortality related to the COVID-19 pandemic in Romania," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-21, December.

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    More about this item

    Keywords

    Count data; Spatial analysis; Spatial heterogeneity;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I10 - Health, Education, and Welfare - - Health - - - General

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