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A Bayesian analysis for pseudo-compositional data with spatial structure.

Edson Z Martinez1, Jorge A Achcar1, Davi C Aragon1

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|July 13, 2019
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Summary
This summary is machine-generated.

This study introduces a Bayesian analysis for pseudo-compositional data, revealing a link between poverty and the age of first-time mothers. Higher poverty correlates with earlier first pregnancies, while lower poverty is associated with later first pregnancies.

Keywords:
Bayesian methodsCompositional dataMCMC methodsepidemiological dataregression models

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Area of Science:

  • Biostatistics
  • Spatial Analysis
  • Demography

Background:

  • Pseudo-compositional data present unique analytical challenges.
  • Maternal age at first birth is influenced by socioeconomic factors.
  • Spatial structures can impact demographic patterns.

Purpose of the Study:

  • To develop a Bayesian framework for analyzing pseudo-compositional data with spatial structures.
  • To investigate the association between poverty and maternal age at first birth in Sao Paulo.
  • To apply log-ratio transformations for compositional data analysis.

Main Methods:

  • Bayesian analysis incorporating a latent factor and spatial structure.
  • Utilized Additive Log-Ratio (alr) and Isometric Log-Ratio (ilr) transformations.
  • Employed Bayesian Markov Chain Monte Carlo (MCMC) simulation for parameter estimation.

Main Results:

  • The isometric log-ratio (ilr) model demonstrated a good fit to the data.
  • Microregions with higher poverty incidence showed a greater proportion of adolescent first-time mothers.
  • Microregions with lower poverty incidence exhibited a higher proportion of women having their first child after age 30.

Conclusions:

  • The Bayesian approach effectively estimated parameters for pseudo-compositional data.
  • Poverty is significantly associated with the age of first pregnancy.
  • The proposed methodology is applicable to various problems involving pseudo-compositional data.