Bayesian Spatial Conditional Overdispersion Models: Application to infant mortality

• Mabel Morales Otero ^[1] ; Vicente Núñez-Antón ^[1]
  1. [1] Universidad del País Vasco/Euskal Herriko Unibertsitatea

     Universidad del País Vasco/Euskal Herriko Unibertsitatea

     Leioa, España

     
• Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 374-377
• Idioma: inglés
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• Resumen

  • In this work we revise Bayesian generalized conditional models for spatial count data with overdispersion. We show their usefulness by tting them to infant mortality rates from Colombian regions. These models assume that the overdispersion present in the data may be caused partially from the spatial dependence that exists among the spatial units. Therefore, regression structures are specied both for the conditional mean and for the dispersion parameter, including also spatial neighborhood structures in the model. We work on the case of spatial count data which follow a Poisson distribution, and focus our attention on the spatial generalized conditional normal Poisson model. Models have been tted with the use of the Markov Chain Monte Carlo (MCMC) algorithms within the context of Bayesian estimation methods.