Bayesian Mixture Poisson Regression for Modeling Spatial Point Pattern of Primary Health Centers in Surabaya

Authors

  • Tri Murniati Sepuluh Nopember Institute of Technology
  • Nur Iriawan Sepuluh Nopember Institute of Technology
  • Dedy Dwi Prastyo Sepuluh Nopember Institute of Technology

DOI:

https://doi.org/10.11113/matematika.v36.n1.1159

Abstract

Primary Health Centres (PHC) are the first referral health facilities for Indonesian people to seek treatment. The varying distribution of PHC location in central Surabaya, therefore, causes its process to follow the Non-homogeneous Poisson Point Process (NHPP). We use Bayesian analysis coupled with Markov Chain Monte Carlo (MCMC) to model the mixture Poisson regression on NHPP intensity estimation. The result shows that two mixture components are significantly involved in the model along with four variables; i.e., the total population, the number of clean households, Accessibility Index, and the length of road; that produce the smallest Deviance Information Criteria (DIC).

 

Keywords: Bayesian Analysis; Mixture Poisson Regression; NHPP Intensity; Primary Health Centres

Author Biographies

Tri Murniati, Sepuluh Nopember Institute of Technology

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Nur Iriawan, Sepuluh Nopember Institute of Technology

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Dedy Dwi Prastyo, Sepuluh Nopember Institute of Technology

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Published

31-03-2020

How to Cite

Murniati, T., Iriawan, N., & Prastyo, D. D. (2020). Bayesian Mixture Poisson Regression for Modeling Spatial Point Pattern of Primary Health Centers in Surabaya. MATEMATIKA, 36(1), 51–67. https://doi.org/10.11113/matematika.v36.n1.1159

Issue

Volume 36 (April 2020)

Section

Articles

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Copyright of articles that appear in MATEMATIKA: MJIAM belongs exclusively to Penerbit UTM Press, Universiti Teknologi Malaysia. This copyright covers the rights to reproduce the article, including reprints, electronic reproductions or any other reproductions of similar nature.