A Bayesian space varying parameter model applied to estimating fertility schedules

Stat Med. 2002 Jul 30;21(14):2057-75. doi: 10.1002/sim.1153.


We propose a spatial generalized linear model (GLM) to analyse the vital rates for small areas. In each small area, we have a response vector and covariates to explain its variability. The statistical methodology is based on a spatial Bayesian approach and it allows the covariates' parameters of the generalized linear model to vary smoothly on space. Hence, the effect of a covariate on the response varies depending on the random variables measurement location. Our model is an extension of disease mapping models allowing the space-covariate interaction to be modelled in a natural way and giving space a position of intrinsic interest. We introduce the model in the context of fertility curve estimation. In each small area, we have a curve describing the variation of fertility rates by age modelled by Coale's fertility model, which implies a GLM in each area. A simulation shows the advantages of our approach. In addition, the paper applies the procedure to census data used to study the diffusion of low fertility behaviour in Brazil.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Bayes Theorem*
  • Birth Rate*
  • Brazil
  • Computer Simulation
  • Female
  • Humans
  • Linear Models*
  • Male
  • Middle Aged
  • Models, Biological*
  • Small-Area Analysis